Proje Destekleri
Deep learning-based segmentation and characterization of optical coherence tomography images in agerelated macular degeneration patients
| Faculty Member | Çiğdem Gündüz Demir, Murat Hasanreisoğlu |
| Project Name | Deep learning-based segmentation and characterization of optical coherence tomography images in agerelated macular degeneration patients |
| Project Description | This project proposes innovative models for the segmentation of retinal layers and fluids in optical coherence tomography images by mathematically incorporating the shape, location, and topology of the relevant regions into the neural network design. It will also perform thickness and volume analysis on the segmented layers and regions to correlate them with age-related macular degeneration disease. |
| Project Start Date | 5.1.2024 |
| Project End Date | 12.1.2025 |
| Number of students to be funded | 2 MS |
| Desired background & experience | Background on computer sciences or related fields. Preferably knowledge on image processing and deep learning algorithms. |
| From which program/s students can be admitted | Computer Science and Engineering; Electrical and Electronics Engineering |
Accuracy, security and scalability improvements for differentially private data collection
| Faculty Member | Mehmet Emre Gürsoy |
| Project Name | Accuracy, security and scalability improvements for differentially private data collection |
| Project Description | In recent years, local differential privacy (LDP) has emerged as a prevalent notion of privacy in the research literature, and it has also been used in products of big tech companies such as Apple, Google, and Microsoft. In this project, students will work on improving the accuracy, security and scalability aspects of data collection under LDP. |
| Project Start Date | 9.15.2023 |
| Project End Date | 9.15.2026 |
| Number of students to be funded | 3 students |
| Desired background & experience | Excellent Python programming skills, strong background in discrete math and probability |
| From which program/s students can be admitted | Computer Science and Engineering |
| Additional information | You may contact the PI (emregursoy@ku.edu.tr) for more info. |
STARLET (ERC PROJECT)
| Faculty Member | SEDA KESKİN |
| Project Name | STARLET (ERC PROJECT) |
| Project Start Date | 8.1.2024 |
| Project End Date | 8.1.2029 |
| Number of students to be funded | 4 PHD STUDENTS |
| Desired background & experience | We are looking for candidates with strong academic backgrounds and research interests in chemical and biomedical applications of materials. |
| From which program/s students can be admitted | Chemical and Biological Engineering; Bio-Medical Sciences and Engineering; Chemistry; Computational Sciences and Engineering; Materials Science and Engineering |
| Additional information | Please see our research group web site: https://mysite.ku.edu.tr/skeskin/ |
| For more information |
High dimensional feature learning in the presence of low-dimensional structure using the universality of empirical risk minimization techniques
| Faculty Member | Zafer Doğan |
| Project Name | High dimensional feature learning in the presence of low-dimensional structure using the universality of empirical risk minimization techniques |
| Project Description | The remarkable success of learning algorithms have revolutionized the field of machine learning. However, theoretical understanding of their learning dynamics is still limited. Moreover, in a large dimensional setting, most of the standard machine learning intuitions (as being originally designed for small dimensional setting) tend to collapse. Therefore, it is necessary to have a deeper understanding of these high dimensional learning problems with several newly emerging phenomenons. Toward such an enlightenment, we consider a fully-connected two-layer neural network that also covers kernel models and nonlinear random features learning model (RFM). Here our goal is to study the generalization performance describing the quality of learning given a task as a function of model complexity in the presence of low-dimensional structure. |
| Project Start Date | 4.1.2024 |
| Project End Date | 4.1.2027 |
| Number of students to be funded | 2 MS and 1 PhD |
| Desired background & experience | A BS/MS degree in electrical and electronics and computer engineering or any relevant field, interest in statistical physics or random matrix theory would be a plus. |
| From which program/s students can be admitted | Electrical and Electronics Engineering; Computer Science and Engineering |
| Additional information | For further information, please contact with Asst. Prof. Zafer Doğan at zdogan@ku.edu.tr |
Adaptive and Reconfigurable Receiver Architectures for Molecular Communications towards Internet of Bio-Nano Things
| Faculty Member | Murat Kuscu |
| Project Name | Adaptive and Reconfigurable Receiver Architectures for Molecular Communications towards Internet of Bio-Nano Things |
| Project Description | The objective of the project is to develop adaptive and reconfigurable dynamic biosensors that could be used as molecular communication receivers in time-varying biochemical environments. The project involves theoretical modeling, simulations, micro/nano device design, micro/nanofabrication, and characterization. |
| Project Start Date | 15.03.2024 |
| Project End Date | 15.03.2027 |
| Number of students to be funded | 2 |
| Desired background & experience | Strong background in multiple of the following subjects: Information and communication theories, signal processing, micro/nanofabrication and characterization |
| From which program/s students can be admitted | Bio-Medical Sciences and Engineering;Electrical and Electronics Engineering;Materials Science and Engineering |
Electromagnetic wave-particle interactions in near-Earth Space
| Faculty Member | Prof. Umran Inan |
| Project Name | Electromagnetic wave-particle interactions in near-Earth Space |
| Project Description | Earth’s atmosphere exists as a result of the balance between the tendency of molecules of air to move upward from regions of higher to lower concentration (diffusion) and downward gravitational pull. Our atmosphere becomes thinner by a factor of [1/e] every 6 km, where e=2.71828 is the Euler’s number.
At at altitudes of about 85 km above the surface of the Earth, the atmosphere has become so thin that molecules of air that are regularly ionized due to cosmic rays and UV radiation from the sun stay ionized because there is not enough air for them to recombine. Earth’s so-called ‘ionosphere’ is thus established as a layer that lies above about 100-km altitude. At even higher altitudes, the atmosphere is completely ionized, and consists of electrons, proton and ions, that freely move under the influence of the Earth’s magnetic field, in this region of near-Earth space that is called the Magnetosphere. In addition to being fully ionized and thus consisting of relatively low energy electrons, protons and ions, this region also hosts extremely high energy (tens of keV to tens of MeV) electrons and protons that are trapped in the Earth’s magnetic field, constituting the so-called ‘radiation belts’. The physics of radiation belts is of great practical interest for our technological environment. All electronic equipment on Earth-orbiting satellites are subject to degredation due to exposure to the high energy radiation, limiting the useful lifetimes of spacecraft. The understanding of the dynamics of the radiation belts is thus of high practical interest. The lifetimes of energetic electrons and protons that constitute the trapped radiation are controlled by high speed injections from the sun in the form of the ‘Solar Wind’ and loss of particles as a result of wave-particle interactions with electromagnetic waves. The physics of these interactions involve some of the most complex manifestations of plasma physics. In June 2019, a major new satellite known as the Demonstration Sciences Experiment (DSX) was launched into a highly elliptical orbit by the SpaceX Falcon/Heavy rocket from Cape Canevaral, Florida. This Spacecraft carried unique Very Low Frequency Transmiter and Receivers, built by Professor Umran Inan’s group at Stanford University, during 2003-2007. DSX was successfully placed in its highly specialized orbit and operated until June 2021, collecting one of a kind data on electromagnetic waves in the near-Earth space environment. Only a small fraction (<10%<) of this data has been analyzed so far, with the rest available in its entirety in the hands of Professor Inan. Between January 2021 through June 2022, Koç University student Ahmet Hamdi Ünal worked on this project under the supervision of Professor Umran Inan, developing specialized MATLAB tools for the analysis of this unique data. Hamdi Ünal has completed his studies at Koç University, graduating in June 2022, with dual degrees in Electrical Engineering and Physics, with ranking 1st in his class in the Electrical Engineering Department and 2nd in his class at Koç University at large. He is now pursuing a PhD in Physics at Cambridge University. To continue this research Professor Inan is seeking a bright and motivated MS and/or PhD student, highly qualified in Physics and Engineering as well as in the usage of MATLAB for Spectral and other analyses. Professor Inan has supervised the PhD theses and graduated 60 PhD students during his career at Stanford, and has served as President of Koç University during 2009-2021. Now that he is retired from his administrative duties, he will be devoting his full time on his academic studies, teaching and research. |
| Desired background & experience | Highly qualified in Physics and Engineering as well as in the usage of MATLAB for Spectral and other analyses |
MI-FI: Micro/Nanoscale Transceivers for Wireless Molecular Information Communications
| Faculty Member | Özgür Barış Akan |
| Project Name | MI-FI: Micro/Nanoscale Transceivers for Wireless Molecular Information Communications |
| Project Description | The project aims to implement the first artificial molecular communication (MC) system with micro/nanoscale transceivers, develop realistic MC techniques based on empirical knowledge gained through experiments on the fabricated MC system prototype, and create practical MC applications having a substantial economic and societal impact. The project start date is May 1st, 2023, and the expected end date is May 1st, 2025, which may be further extended. |
| Project Start Date | 1.05.2023 |
| Project End Date | 1.05.2025 |
| Number of students to be funded | Several MSc, PhD, Postdoc, and Research Assistant Professor positions are available. For postdocs and research assistant professors, flexible and remote working arrangements can be made. Up to 15.000 TL monthly net scholarship plus other benefits (e.g., meal card, health insurance) for PhD students. For postdocs and research assistant professors, the salary will be determined based on experience and skill set. Other benefits also apply. |
| Desired background & experience | Required qualifications for this position include excellent quantitative skills, e.g., a sound grasp of mathematics and proficiency in communication and information theoretical tools. Previous experience in communication, information theoretical modelling of molecular communications, and computer programming in a scientific research context, such as C++ or MATLAB, is essential. Additionally, interest in or experience in micro-nanofabrication is a plus. Strong problem-solving abilities and the ability to work independently, using their initiative, and collaboratively as part of a team are essential qualifications for this role. The qualifications mentioned previously are required for higher-grade posts. The additional requirements are as follows: a proven ability to identify funding sources and contribute to the process of securing funds, along with a solid academic background and publications in relevant fields. Other essential qualifications for this role include the ability to quickly familiarize themselves with relevant fields outside their expertise, excellent organizational skills, experience in managing junior staff, and managing workload effectively to meet deadlines. |
| From which program/s students can be admitted | Bio-Medical Sciences and Engineering;Electrical and Electronics Engineering;Materials Science and Engineering |
| Additional information | Contact PI (akan@ku.edu.tr) with your CV for further details. |
Investigation of E2-E3 HECT ligase cryptic intermediate step interactions between E6AP and UbcH7 in solution and data-driven modeling
| Faculty Member | Çağdaş Dağ |
| Project Name | Investigation of E2-E3 HECT ligase cryptic intermediate step interactions between E6AP and UbcH7 in solution and data-driven modeling |
| Project Description | Ubiquitin (Ub) and ubiquitin-like (Ubl) proteins play an important role in the control of cellular processes by being conjugated to various target proteins via an enzymatic cascade. The most well-known of these modification systems is ubiquitination. A type of post-translational modification called ubiquitination; It is regulated by enzymes called E1, E2 and E3. E3 enzymes, which have more than 600 types in humans, interact with different E2 enzymes and provide the transfer of Ub or Ubl proteins to the target proteins. This transfer varies in different classes of E3 enzymes. HECT ligases, one of the E3 enzyme classes, take over ubiquitin from E2 and transfer it onto themselves. Following this transfer, Ub is transferred to the target protein. The E3 HECT enzyme family consists of 28 members. One of the widely studied members of these enzymes is the E6AP enzyme. Although the E6AP enzyme is found as a trimer in its crystal structure, the general opinion is that HECT (E3) enzymes work as monomers, since other HECT-type enzymes are monomers in their crystal structures. In new studies biochemical data have been obtained showing that E6AP HECT ligase works as an oligomer instead of a monomer, and that it interacts with UbcH7 (E2) from two different regions instead of one to form polyubiquitin chains. This situation, which is very difficult to be clarified by crystal structures due to the low affinity of the second binding site (cryptic binding site), requires the use of techniques in which proteins are studied in solution. For this reason, the oligomerization dynamics of the HECT domain of the E6AP enzyme and the UbcH7 (E2) binding region of the E6AP enzyme on the HECT domain will be examined for the first time by NMR (Nuclear magnetic resonance) and SAXS (Small angle X-ray scattering) techniques and interpreted with the HADDOCK program. Method: Recombinant E6AP will be produced using 15N isotopic labeled and unlabeled E. coli to study E6AP oligomerization. Unlabeled E6AP enzymes will be examined by SAXS analysis, while uniformly labeled protein will be examined by NMR using the two-dimensional DOSY method. In order to determine the cryptic binding site on the E6AP HECT domain, recombinant protein production will be performed by ILVA selective isotopic labeling method. In this way, the methyl groups of ILVA amino acids in the HECT domain of the E6AP enzyme will be marked isotopically and the 2, 3 and 4-dimensional NMR spectra required for the resonance assignments of ILVA methyl groups will be collected. After the resonance assignments, the changes in the chemical shift values of the atoms will be determined by adding the UbcH7 enzyme at varying concentrations to the solution containing the isotopic labeled E6AP enzyme. With this method, data about the cryptic binding surface involved in the E2-E3 interaction will be obtained by NMR spectroscopy. In the final phase of the project, a much more realistic modeling will be made for the experimental data-based in-silico E2-E3 cryptic interaction using the data obtained from the SAXS and NMR experiments. Management: All researchers who are partners of the project have many years of experience in education, training and research in their fields, and these experiences will create a great synergy in the project. Dr. Dağ, during his career worked on the biological and biochemical applications of NMR spectroscopy. He carried out studies about structure-function relationship of biological systems such as G-protein coupled receptors, beta-arrestin, proteins involved in the formation mechanisms of iron-sulfur clusters by NMR spectroscopy. Dr. Karaca has many years of experience in computational structural biology, took part in the team that created integrated structure determination software such as HADDOCK, M3 and has expertise in information-driven modeling. Dr. Kaplan-Türköz has extensive experience in Small-angle X-ray scattering and X-ray crystallography in solution and has extensive experie |
| Project Start Date | 10.15.2022 |
| Project End Date | 10.15.2025 |
| Number of students to be funded | 1 |
| Desired background & experience | Chemistry;Chemical and Biological Engineering;Molecular Biology and Genetics;Bio-Medical Sciences and Engineering |
Enabling in-situ thermomechanical studies on Si nanoWIREs – WireOn
| Faculty Member | B. Erdem Alaca |
| Project Name | Enabling in-situ thermomechanical studies on Si nanoWIREs – WireOn |
| Project Description | Wire-On, the collaborative project between Turkish and German (Fraunhofer-IKTS) research institutions, addresses the fundamental problem of scale dependence of brittle to ductile transition (BDT) temperature of silicon (Si) single-crystal nanowires as a reliability parameter for future devices and systems. |
| Project Start Date | 9.1.2023 |
| Project End Date | 2.28.2025 |
| Number of students to be funded | 1 PhD student |
| Desired background & experience | Background in mechanical engineering, materials engineering; interest in nano-fabrication, nano-modeling. |
| From which program/s students can be admitted | Mechanical Engineering;Materials Science and Engineering |
| Additional information | As a member of our team, you will have access to state-of-the-art facilities and the chance to collaborate closely with experienced researchers in Mechanical Characterization Laboratory (MCL) (https://mcl.ku.edu.tr/) and Koç University Nanofabrication and Nanocharacterization Center for Scientific and Technological Advanced Research (n2STAR) (https://n2star.ku.edu.tr/). Our work spans three related fields: nano-fabrication, nano-analysis, and nano-modelling, providing a comprehensive and interdisciplinary research experience. If you are driven, passionate about science, and committed to making a meaningful contribution to the field of nanotechnology, we encourage you to apply for this position. For more info please contact mkarimzadeh@ku.edu.tr or ealaca@ku.edu.tr. |
Identifying Cancer Cell-Secreted Proteins Active in Tumor Macro-Environment
| Faculty Member | SERKAN KIR |
| Project Name | Identifying Cancer Cell-Secreted Proteins Active in Tumor Macro-Environment |
| Project Description | |
| Project Start Date | 4.15.2023 |
| Project End Date | 4.15.2025 |
| Number of students to be funded | 3 PhD students |
| Desired background & experience | Molecular Biology |
| From which program/s students can be admitted | Molecular Biology and Genetics |
GoodMobility (ERC Project)
| Faculty Member | Barış Yıldız |
| Project Name | GoodMobility (ERC Project) |
| Project Description | Several social, economic, and technological trends are coming together to revolutionize the way people, products, and services are brought together. Goods are gaining more mobility with drastic implications for cities. The current city logistics (CL) paradigm, which does not consider the mobility of goods a public need, limits our capacity to understand and respond to the challenges and opportunities brought by this profound change. Taking a different perspective, GoodMobility proposes to replace the techno-business-centric smart thinking with network-centric wise logistics that takes the public value as the primary goal and envisions setting up a logistics web with the required infrastructure (hardware) and operating procedures (software) to establish logistics as a service (LaaS). The first objective is to develop public value as a measurement system to assess and guide CL planning and management, which will be achieved with an iterative process, combining multi-criteria decision making, sustainability analysis, simulation, and optimization. The second objective is to develop the principles, models, and tools for LaaS infrastructure design. Novel network optimization methods will be developed for designing the hardware of LaaS to provide orders of magnitude efficiency gains and influence the stakeholder activities to maximize public value. The third objective is to develop a theoretical framework and models for the operating procedures of LaaS, introducing the logistics markets to ensure efficiency and reliability and secure public value in matching logistics demand and supply. Learning-based optimization methods will be developed to manage logistics markets. If successful, GoodMobility will lay the foundations of a new theory of CL with significant scientific and practical implications. The novel ideas, concepts, and methodologies will open up new research perspectives in transport and logistics with far-reaching social, economic, and environmental consequences |
| Project Start Date | 1.1.2023 |
| Project End Date | 1.1.2028 |
| Number of students to be funded | 3 PhD students |
| Desired background & experience | We are looking for candidates with strong academic backgrounds and research interests in one or more of the following areas. – New business models and technologies in last-mile delivery – Transportation network design and management – Intelligent transportation systems – Behavior modeling – Multicriteria decision making – Sustainability analysis – Sequential decision making – Agent-based simulation Candidates with experience in GIS tools, traffic simulations, discrete choice modeling, and data-driven decision-making are strongly encouraged to apply. Experience in multi-disciplinary and international research projects is a plus. |
| From which program/s students can be admitted | Industrial Engineering and Operations Management |
Machine-learning-driven bottom-up design of atomically-layered heterostructures for green H2 production
| Faculty Member | Dr. Esmail Doustkhah and Prof. Dr. Sarp Kaya |
| Project Name | Machine-learning-driven bottom-up design of atomically-layered heterostructures for green H2 production |
| Project Description | The project is about synthesis of thin films guided by machine learning and DFT for electro- and photocatalytic carbon-free production of H2. This project funded to circulate an international collaboration among Japan, Spain, and Turkey toward an atomically precise methods for fabrication of thin film heterostructures for green H2 generation. The candidate will closely work with Japanese, and Spanish partners to develop a ML-coupled DFT-guided heterostructures using some methods such as chemical vapor deposition. The successful candidate must write a progress of report, hands-on in experiments and good understanding in DFT to apply them in the experimental synthesis and should have good knowledge about crystallography and photocatalysis. For any further information, please contact through the following email: edosutkhahheragh@ku.edu.tr |
| Project Start Date | |
| Project End Date | 8.1.2026 |
| Number of students to be funded | One PhD |
| Desired background & experience | -Photocatalysis; -Electrocatalysis; Thin film fabrication; Crystallography; Heterojunction |
| From which program/s students can be admitted | Materials Science and Engineering;Chemistry |
| Additional information | The European Interest Group (EIG) CONCERT-Japan is an international joint initiative to support and enhance science, technology and innovation (STI) cooperation between European countries and Japan. The EIG CONCERT-Japan is flexible and inclusive in nature, able to accommodate a range of forms of collaboration from unanimous concerted efforts to optional participation among its core members and other interested STI institutions. |
Tailoring the interface of perovskite/electron transport layers by employing nanostructured CdS for efficient perovskite solar cells
| Faculty Member | Hamaneh Zarenezhad, Sarp Kaya |
| Project Name | Tailoring the interface of perovskite/electron transport layers by employing nanostructured CdS for efficient perovskite solar cells |
| Project Description | In perovskite solar cells (PSCs) not only the formation of full coverage perovskite layer with a compact, uniform, and large crystal size morphology is a considerable factor to get high device performance, but also efficient charge carrier extraction and transport without nonradiative recombination in the perovskite layer and interfaces have a crucial role in the photovoltaic performance. In this regard, the electron transport layer (ETL) plays an important role in enabling high photovoltaics in perovskite solar cells. Therefore, the selection of an appropriate ETL as a substrate for perovskite coating can significantly modulate the morphology of the perovskite active layer, the transfer, and extraction of the charge carriers, as well as the efficiency of the PSC. The main aim of the proposed research is to design a more effective ETL and perovskite film in PSC structure for increasing the efficiency and stability of PSCs. |
| Project Start Date | 4.1.2023 |
| Project End Date | 10.1.2025 |
| Number of students to be funded | Two (MS or PhD) |
| Desired background & experience | Graduated in materials sciences, chemistry, or related fields. Experience in synthesis, fabricating perovskite solar cell, photovoltaic and optoelectrical characterization preferred. |
| From which program/s students can be admitted | Chemistry;Materials Science and Engineering |
Active Photoelectrode Development for Green Hydrogen Production via Solar Overall Water Splitting
| Faculty Member | Sarp Kaya |
| Project Name | Active Photoelectrode Development for Green Hydrogen Production via Solar Overall Water Splitting |
| Project Description | Heterostructured photoelectrode development and characterization for green hydrogen generation |
| Project Start Date | 4.2.2022 |
| Project End Date | 3.31.2025 |
| Number of students to be funded | 1 MS or PhD |
| Desired background & experience | Chemistry, Physics, Material Science and Engieneering, Chemical Engineering, |
| From which program/s students can be admitted | Materials Science and Engineering;Chemistry |
TÜBİTAK - Identifying the Performance of Covalent Organic Frameworks for Separation of Carbon Dioxide from Natural Gas by Integrating High-throughput Computational Screening and Machine Learning Techniques
| Faculty Member | SEDA KESKİN AVCI |
| Project Name | Identifying the Performance of Covalent Organic Frameworks for Separation of Carbon Dioxide from Natural Gas by Integrating High-throughput Computational Screening and Machine Learning Techniques |
| Project Description | Machine learning applications of porous materials |
| Project Start Date | 9.15.2022 |
| Project End Date | 9.15.2025 |
| Number of students to be funded | 2 PhD 1 MS |
| Desired background & experience | Chemical engineering, computer science, chemistry, materials science |
| From which program/s students can be admitted | Chemical and Biological Engineering;Computational Sciences and Engineering |
Reformulation Approaches in Multiobjective Discrete Optimization
| Faculty Member | Serpil Sayın |
| Project Name | Reformulation Approaches in Multiobjective Discrete Optimization |
| Project Description | Multiobjective optimization (MOO) refers to optimization of multiple functions simultaneously over a given feasible set. Solving a MOO problem exactly requires finding the entire Pareto Set. The computational cost of obtaining this set increases with problem size and is particularly sensitive to the number of objective functions. The project aims to develop reformulation approaches for a MOO problem with a smaller number of objective functions. |
| Project Start Date | 10.3.2022 |
| Project End Date | 10.3.2025 |
| Number of students to be funded | one |
| Desired background & experience | A strong mathematical background, familiarity with fundamentals of optimization and prior computing experience is required. The candidate should have a keen interest in methodology development, algorithm design and implementation. |
| From which program/s students can be admitted | Industrial Engineering and Operations Management |
Human Intractome Project: Structural protein-protein interaction resource at genomic scale
| Faculty Member | Ozlem Keskin |
| Project Name | Human Intractome Project: Structural protein-protein interaction resource at genomic scale |
| Project Description | Perhaps one of the most surprising results of genome projects was that the number of genes in humans was less than expected. This result showed that the biological complexity of organisms does not only depend on the number of genes. Number of protein interactions, alternative splicing post-translational modifications are some factors that might contribute to the complexity. At the molecular level, all biological processes within a cell occur as a result of interactions among proteins, DNA/RNA and small molecules. Protein-protein/molecule interactions play the most important role in signaling and metabolic pathways. All of the binary protein-protein interactions in the cell form the interactome. It is of importance to examine the interactome, which shows the relationship between proteins as a whole, to interpret protein functions and to understand complex cellular processes, to understand the events that cause diseases and to understand genotype-phenotype relationships. In summary, the interactome can allow the biological processes occurring in the cell to be seen as a whole. Even though Interactome is very important to give general information about processes, it is not sufficient in designing targeted drugs or controlling cellular processes, and remains abstract. Proteins form physical complexes with each other, and these complexes form regulatory and signaling pathways. Therefore, finding out how proteins interact at the molecular and atomic levels in three-dimensional space will give us concrete information. We conducted studies to predict protein-protein interactions with PRISM method developed in our group. With PRISM, which has been active as a web server since 2005, we can answer questions such as whether two proteins interact with each other, if so using which amino acids are used and, how they bind (http://cosbi.ku.edu.tr/prism/). This method is the first and pioneering study in the literature that finds protein-protein interactions using template interfaces. Later, many studies, similar to our method, were carried out. PRISM method/application publications have received around 3000 citations so far. Since experimental studies cover 1-10% of human interactome, it is extremely important to find interactome with reliable computational methods. The main purpose for finding the interactome is to understand the biological processes in the cell, the causes of the diseases in depth and to find rational solutions in the treatment of the diseases. The main aim of this project is to extract the comprehensive human interactome, integrate it with the knowledge of 3-dimensional structure (experimental or homology model), make the interactions more physical and meaningful, and present the most comprehensive and reliable structural interactome resource in the literature to the scientific community. In addition, multiple annotations will be integrated into this interactome. Mapping disease mutations on the proteins will enable us to see which mutation causes which disease as a whole. In the project, tissue-specific intercatomes will also be obtained, the time dimension will be added to the interactomes, post-translational modifications (such as phosphorylation) will be labeled on the protein structures, and the effects of these on the interactions will be indicated. |
| Project Start Date | 7.1.2021 |
| Project End Date | 7.1.2024 |
| Number of students to be funded | 5 phd 1 postdoc |
| Desired background & experience | |
| From which program/s students can be admitted | Bio-Medical Sciences and Engineering;Chemical and Biological Engineering;Computational Sciences and Engineering;Computer Science and Engineering;Molecular Biology and Genetics |
Product Line Design and Pricing Policies in the Presence of Variability in Use Phase Behaviors of Consumers and Extended Producer Responsibility Programs
| Faculty Member | Selçuk Karabatı |
| Project Name | Product Line Design and Pricing Policies in the Presence of Variability in Use Phase Behaviors of Consumers and Extended Producer Responsibility Programs |
| Project Description | As a result of increased sustainability awareness, some consumer segments’ product selection processes have evolved to include, in addition to traditional factors such as price, the environmental impact of products as a significant criterion. This behavioral shift has increased the demand for green products. In a multi-segment market where customers have heterogeneous product valuations and environmental sensitivity levels, companies must perform a delicate balancing act of the financial and environmental objectives. In the first part of the project, a firm’s product line design and pricing problem will be studied. The firm operates in a heterogeneous market. The customer segments’ product valuations and sensitivity to products’ environmental impacts will be assumed to vary, and a rational consumer response model that captures these variabilities will be developed. An integrated optimization model will then be presented by embedding the developed consumer model into the firm’s product line design and pricing problems. The model will be further expanded by integrating regulatory mechanisms governments can impose to achieve their intended sustainability goals, such as Extended Producer Responsibility programs. The inclusion of green products in the product lines of the firms that serve the retail market is a clear indicator of promising changes in the extent of environmental awareness. For example, in addition to conventional laundry detergents, ultra-concentrated detergents have quickly taken their place in retail stores. On the other hand, because of consumer behaviors observed in the use phase, a green product that has been designed to have a smaller environmental impact may end up having a larger impact than its traditional counterparts. Therefore, a model that does not consider the differences in consumers’ behaviors in the use phase of green products cannot successfully guide a firm’s product line design, pricing, and product design strategies. The project aims to develop a model that can also capture the negative environmental effects that consumers may cause when using green products. In consideration of the size of the retail industry, it is evident that such an integrated model can bring about value not just in the context of effective product design and pricing policies, but also in the management of the environmental value chain. |
| Project Start Date | 3.04.2023 |
| Project End Date | 1.10.2025 |
| Number of students to be funded | 2 |
| Desired background & experience | Strong foundation in mathematical programming and optimization techniques. Familiarity with optimization and symbolic computation software. |
| From which program/s students can be admitted | Industrial Engineering and Operations Management |
NanoBio
| Faculty Member | Erkan Şenses |
| Project Name | NanoBio |
| Project Description | We develop nanocomposite hydrogels from plant-based sustainable resources |
| Project Start Date | 3.1.2023 |
| Project End Date | 9.1.2025 |
| Number of students to be funded | 1 PhD |
| Desired background & experience | Chemistry, Chemical Engineering, Materials Science |
| From which program/s students can be admitted | Chemical and Biological Engineering;Chemistry;Materials Science and Engineering;Molecular Biology and Genetics |
| Additional information | softmatter.ku.edu.tr |
Nanomaterials for bioelectronics/optoelectronics
Faculty MemberSedat NizamogluProject NameNanomaterials for bioelectronics/optoelectronicsProject DescriptionWe will develop new nanomaterials such as quantum dots for optoelectronic applications such as light-emitting diodes and luminescent solar concentrators. We will also develop also new nanomaterials for photostimulation of neurons.Project Start Date9.1.2022Project End Date9.1.2027Number of students to be funded2Desired background & experienceMaterial Science and EngineeringFrom which program/s students can be admittedMaterials Science and Engineering; ChemistryAdditional informationWe will support with the highest amount of scholarship.
Bioelectronic Devices for Neuron Activity Control Against Blindness
| Faculty Member | Sedat Nizamoglu |
| Project Name | Bioelectronic Devices for Neuron Activity Control Against Blindness |
| Project Description | We will develop a new kind of bioelectronic device that will stimulate the neurons in the retina against blindness. The devices will absorb the light energy and photostimulate the neurons. The devices will be fabricated via solution-processing methods and also in the clean-room via microfabrication; their electrical, optical and photochemical properties will be characterized and they will be tested on neurons by using electrophysiology systems. Finally, we will test them on animals and if successful, we will translate it to patients. We will fully teach all the fundamentals to the newly coming graduate students. |
| Project Start Date | 9.1.2022 |
| Project End Date | 9.1.2027 |
| Number of students to be funded | 2 |
| Desired background & experience | Electrical & Electronics Engineering |
| From which program/s students can be admitted | Bio-Medical Sciences and Engineering; Electrical and Electronics Engineering;Physics |
| Additional information | We will provide the highest level scholarship to the students with housing, meal card, etc. |
Network design and management problems for social aid distribution operations
Faculty MemberBarış YıldızProject NameNetwork design and management problems for social aid distribution operationsProject DescriptionThe lack of an infrastructure to facilitate the fast and economical distribution of social aid is one of the main obstacles to enhancing the scope and the quality of the social aid efforts, which constitute one of the main pillars of solidarity and unity in our societies. Especially in large cities, the difficulties related to last-mile delivery operations (high economic and environmental cost) results in spending a significant part of the aid resources on delivery expenditures or blocking a substantial in-kind donation potential. In this project, we aim to address this critical issue and develop a novel social aid distribution network that takes advantage of the emerging smart transportation applications.Project Start Date4.1.2022Project End Date4.1.2025Number of students to be funded1 MS and 1 PhD students will be hiredDesired background & experienceStudents are expected to have a solid background in math programming, algorithm design and basic data analysis. Candidates with coding experience and competence in data visualization tools will be given priority.From which program/s students can be admittedIndustrial Engineering and Operations Management
SynergyNet: Internet of Energy with Blockchain, Smart Contract and Federated Learning
Faculty MemberÖznur ÖzkasapProject NameSynergyNet: Internet of Energy with Blockchain, Smart Contract and Federated LearningProject DescriptionInternet of Energy is an innovative and effective approach that integrates the concept of smart network and Internet technology. Unlike traditional centralized energy systems, distributed Energy Internet system with multiple components and communication requirements needs innovative technologies for reliability and efficiency. Emerging and promising, distributed blockchain, smart contracts, and distributed federated learning technologies offer new opportunities for decentralized Energy Internet systems. Our objective in the SynergyNet project is to develop effective system models, techniques and algorithms by applying innovative distributed blockchain, smart contract and distributed federated learning principles to key research problems and areas within the Energy Internet. SynergyNet project is funded by TÜBİTAK 2247-A National Research Leaders program research grant. Fully funded PhD student and Postdoctoral researcher positions are available.Project Start Date10.1.2022Project End Date10.1.2025Number of students to be funded4 PhD Students, 1 Postdoctoral ResearcherDesired background & experienceDistributed systems, Internet of Energy, Blockchain, Intelligent systems, Distributed federated learning, peer-to-peer (P2P) networks.From which program/s students can be admittedComputer Science and EngineeringAdditional informationFor detailed information, please contact PI (oozkasap@ku.edu.tr) with your CV.
Active Photoelectrode Development for Green Hydrogen Production via Solar Overall Water Splitting
| Faculty Member | Sarp Kaya |
| Project Name | Active Photoelectrode Development for Green Hydrogen Production via Solar Overall Water Splitting |
| Project Description | Using solar light to split the water by photo-electrochemical means to obtain hydrogen, to store the hydrogen or to catalytically convert the materials that can be used as direct fuel, is a way to efficiently remove the energy problem. The main objective of the project is to develop a photoelectrochemical cell for solar hydrogen production via overall water splitting. |
| Project Start Date | 4.1.2022 |
| Project End Date | 7.31.2024 |
| Number of students to be funded | 1 MS and 1 PhD |
| Desired background & experience | Chemistry, Material Science, Material Physics, Chemical Engineering |
| From which program/s students can be admitted | Chemistry; Materials Science and Engineering; Physics |
Machine Learning and Extreme Value Theory based Ultra Reliable Communication for Wireless Control Systems
| Faculty Member | Sinem Coleri |
| Project Name | Machine Learning and Extreme Value Theory based Ultra Reliable Communication for Wireless Control Systems |
| Project Description | Safety critical control applications still cannot leverage the advances in Wireless Internet of Everything (IoE) for two main reasons: 1) Lack of methodologies for guaranteeing ultra-reliable low latency wireless communication based on a statistical model of the environment, 2) challenge of integrating ultra reliable communications into wireless control systems. In this project, our goal is to develop innovative methods based on machine learning and extreme value theory for wireless channel modeling, communication and control systems in the ultra-reliable region in 6G and beyond systems. Extreme value theory (EVT) is a unique statistical discipline that develops techniques and models to describe rare events. On the other hand, machine learning provides efficient solutions for the challenging problems that cannot be solved with traditional analytical approaches in the design of next generation communication systems of complex and heterogenous nature. Large amounts of data are used in the extraction of ultra-reliable channel statistics and the optimization problem in which ultra-reliable communication is integrated into the control systems is very challenging, thus, the use of EVT together with machine learning techniques may enable ultra-reliable control systems. |
| Project Start Date | 6.1.2022 |
| Project End Date | 6.1.2025 |
| Number of students to be funded | 4 Ph.D students, 1 postdoctoral researcher |
| Desired background & experience | Wireless networks, machine learning |
| From which program/s students can be admitted | Electrical and Electronics Engineering |
2ND CHANCE (ERC Project)
| Faculty Member | Levent Beker |
| Project Name | 2ND CHANCE (ERC Project) |
| Project Description | medical device development |
| Project Start Date | 1.05.2022 |
| Project End Date | 1.04.2027 |
| Number of students to be funded | 2 MS, 3 PhD, 2 Postdoc |
| Desired background & experience | Electrical Engineering, Mechanical Engineering, Materials Science, Chemistry, Medicine, or related fields |
| From which program/s students can be admitted | Bio-Medical Sciences and Engineering, Chemistry, Electrical and Electronics Engineering, Materials Science and Engineering, Mechanical Engineering, Molecular Biology and Genetics |
| Additional information | contact PI with your CV for further details. web site: microdevices.ku.edu.tr |
Deep learning-enabled crowd density estimation for cell analysis in digital pathology and characterization of homologous recombination deficiency in high-grade ovarian serous carcinoma
| Faculty Member | Çiğdem Gündüz Demir |
| Project Name | Deep learning-enabled crowd density estimation for cell analysis in digital pathology and characterization of homologous recombination deficiency in high-grade ovarian serous carcinoma |
| Project Description | This project aims to automatically segment and characterize cell nuclei in pathology slides using deep learning models and discover correlation between the nucleus characteristics and the homologous recombination deficiency status in high-grade ovarian serous carcinoma. |
| Project Start Date | 1.11.2021 |
| Project End Date | 1.11.2024 |
| Number of students to be funded | 2 students |
| Desired background & experience | Experience in designing and training convolutional neural networks, including encoder-decoder networks, and working on cell segmentation is a plus. |
| From which program/s students can be admitted | Computational Sciences and Engineering, Computer Science and Engineering, Electrical and Electronics Engineering, Bio-Medical Sciences and Engineering |
MiMoGraph: Microfluidic Molecular Communications with Graphene-based Two-dimensional Nanoscale Receivers for Internet of Nano Things
| Faculty Member | Murat Kuscu |
| Project Name | MiMoGraph: Microfluidic Molecular Communications with Graphene-based Two-dimensional Nanoscale Receivers for Internet of Nano Things |
| Project Description | The objective of MiMoGraph is to develop the first micro/nanoscale experimental test and validation platform for microfluidic molecular communication (MC) systems accompanied by a theoretical design, modeling and optimization framework, and to devise novel experimentally-validated low-complexity MC methods, such as modulation, coding, synchronization, and detection techniques. |
| Project Start Date | 1.06.2021 |
| Project End Date | 31.05.2024 |
| Number of students to be funded | 2 MSc and/or PhD |
| Desired background & experience | |
| From which program/s students can be admitted | Bio-Medical Sciences and Engineering, Electrical and Electronics Engineering, Materials Science and Engineering |
Computational Modeling of Turbulent Spray Combustion of Alternative Liquid Fuels Under High Pressure Conditions
| Faculty Member | Metin Muradoglu |
| Project Name | Computational Modeling of Turbulent Spray Combustion of Alternative Liquid Fuels Under High Pressure Conditions |
| Project Description | In this project, we aim to investigate the characteristics of the alternative liquid fuels under the conditions relevant to the aerospace gas turbine combustion chambers. Additionally, we aim to draw the fundamental knowledge required to design robust and efficient aerospace engine operating with alternative liquid fuels. Aerospace gas turbine combustion chambers feature the operating conditions having high pressure ambient condition, highly swirling flows, and intense turbulentchemistry interactions. In aerospace gas turbine combustion chambers, the oscillating heat release rate and the pressure fluctuation may interact and results in combustion instabilities which may cause severe damages to the engine. Therefore, the effect of the liquid fuels on the performance of the aerospace gas turbines must be characterized and understood. In literature the effect of the fossil fuels has been studied extensively, however the studies on the effect of alternative liquid fuels under aerospace gas turbine operation conditions have been rather scarce. In this project, the effect of the alternative liquid fuels on the performance of the gas turbine combustion chamber, and the interaction of the alternative liquid fuels with the highly non-linear flow dynamics will be investigated using numeric models. An extensive parametric study will be performed on different operating conditions in order to draw the characteristics of the alternative liquid fuel under the conditions relevant to the aerospace gas turbine operating conditions. To do that, as a first step, high-fidelity numeric solution algorithms will be developed for the numerical solution of the turbulent spray combustion flames. Comprehensive validation studies will be performed for the assessment of the developed numerical methods. In this project two different turbulent combustion models will be employed: i) the transported probability density function (tPDF) model, and ii) the flamelet generated manifold method (FGM). In tPDF method, the highly nonlinear chemical source terms appear to be in closed form, and there is no intrinsic assumption on the structure of the turbulent flame. Thus, the tPDF method can accurately model turbulence-combustion interactions, and resulting local extinction and reignition, emission, soot formation etc. Additionally, tPDF allows to draw very detailed analyzes on the flame structure due to existence of a very large number of Lagrangian particles. However, the computational cost of the tPDF method is high compared to the other combustion models, thus its usage in industrial design applications is limited. FGM method relies on the assumption that multi-dimensional structure of the turbulent flames can be considered as the assemble of onedimensional laminar flames, which significantly reduces the computational time required for simulation of turbulent flames. In the literature FGM method has been applied to simulate a wide range of turbulent flames in variety of combustion regimes. The main motivation of using these methods is i) advanced features of the tPDF method will enable us to make a very comprehensive analyzes and to draw detailed characteristics of the alternative liquid fuels, which is difficult to achieve in the reduced combustion models if not impossible, ii) FGM method has the capability of predicting the main flame features (temperature, concentration of the main species) in reasonable computation cost, which makes it very attractive for industrial design applications. In this study the validity and performance of FGM method will be assessed for the alternative liquid fuel combustions. |
| Project Start Date | 15.11.2021 |
| Project End Date | 15.11.2024 |
| Number of students to be funded | 2 MS and 2 PhD |
| Desired background & experience | Strong background in thermofluids sciences and math is required, and some background in computational methods and CFD is a plus. |
| From which program/s students can be admitted | Mechanical Engineering, Computational Sciences and Engineering |
ERC-BeyondMoore
| Faculty Member | Didem Unat |
| Project Name | ERC-BeyondMoore |
| Project Description | The ERC (European Research Council) project has the overall objective to solve software side of the Post-Moore’s Law problem. The project will develop parallel programming models, performance models, compiler and runtime technologies designed for hardware accelerators. The implementations will be tested on large scale applications coming from deep learning, graph analytics, and high performance computing. |
| Project Start Date | 2.08.2021 |
| Project End Date | 2.08.2026 |
| Number of students to be funded | 2-3 |
| Desired background & experience | Required profile for Postdoc Positions All applicants must hold a PhD degree in computer science or related fields. The applicants should also have a strong track record in scientific publication and dissemination. The applicant should be fluent in both spoken and written English. All applicants must have research expertise in at least two of the following subjects: Parallel programing or Accelerator programming (e.g., OpenMP, CUDA, MPI) Performance optimization and performance modeling Scalable graph neural networks and deep learning Computer architecture Operating systems or system software Sparse Computation and Sparse Matrices Required profile for PhD Positions BSc in Computer Science/Engineering or related fields Applicants should have strong knowledge in Operation Systems, Computer Architecture, Data Structures and Algorithms No MSc is required to apply for the PhD position |
| From which program/s students can be admitted | Computer Science and Engineering |
SectionImmune Cell Signaling in Situ and in Real Time
| Faculty Member | Serap Aksu |
| Project Name | Immune cell signaling in situ and in real time |
| Project Description | The aim of the EMBO (European Molecular Biology Organization) funded project is to develop programmable optofluidic platforms to investigate cell secretion parameters and understand the dynamics of cell signaling, even at single cell level. The platform is composed of photonic nanosized biosensors and a multi-layer, automated microfluidic platform that enables real-time and in-situ investigation of any kind of cells with utmost ability. |
| Project Start Date | 1.1.2022 |
| Project End Date | 1.1.2025 |
| Number of students to be funded | 1 PhD and 1 Postdoc |
| Desired background & experience | Background in molecular biology, cell culturing or bioengineering; interest in micro-nanofabrication, microdevice generation and spectroscopy. |
| From which program/s students can be admitted | Bio-Medical Sciences and Engineering, Materials Science and Engineering, Molecular Biology and Genetics, Electronics Engineering/ Physics |
| Additional information | You will be using microfabrication laboratory and imaging facility to design and perform experiments. For more info please contact saksu@ku.edu.tr. |
Functional characterization of enhancer activity in late stage prostate cancer
| Faculty Member | Nathan Lack |
| Project Name | Functional characterization of enhancer activity in late stage prostate cancer |
| Project Description | This computational project will characterize how changes in enhancer activity drive late-stage prostate cancer. Working closely with international collaborator we will analyze and integrate large-scale functional genomic data. |
| Project Start Date | 1.09.2022 |
| Project End Date | 1.08.2025 |
| Number of students to be funded | There is funding for at least two PhD/MSc students |
| Desired background & experience | Ability to code in either R or Python; Strong understanding of molecular biology and functional genomic techniques; Ability to work independently; Strong problem solving abilities |
| From which program/s students can be admitted | Molecular Biology and Genetics, Bio-Medical Sciences and Engineering, Computational Sciences and Engineering, Computer Science and Engineering, Physics |
| Additional information | Please contact me at nlack@ku.edu.tr for additional information. |
Interplay between organelle and chromosome segregation during cell division
| Faculty Member | Ayşe Koca Çaydaşı |
| Project Name | Interplay between organelle and chromosome segregation during cell division |
| Project Description | Eukaryotic cells divide through mitosis to produce new cells with identical DNA. For this replicated chromosomes have to be equally segregated to the daughter cell. In addition to segregation of chromosomes, segregation and/or reorganization of other organelles are crucial for fidelity of cell division. These events must be carefully coordinated and controlled for maintenance of cell-ploidy and viability. Our lab is particularly interested in understanding how mitosis communicates with segregation of other organelles. We are particularly focused at the cross-talk of lysosome segregation with the process of mitotic exit. The inherent asymmetry and genetic malleability of the unicellular budding yeast Saccharomyces cerevisiae, maintain it at the forefront of model systems in which to dissect cell cycle regulation. We use this excellent model organism in our research. Our research integrates cutting-edge microscopy, high-throughput genetics and state-of-the-art proteomics techniques to uncover complexities of mitosis. By revealing novel mechanisms of cell cycle control and genomic stability we hope to contribute discovery of novel targets for cell cycle related diseases such as cancer. |
| Project Start Date | 1.09.2022 |
| Project End Date | 1.09.2026 |
| Number of students to be funded | 3 |
| Desired background & experience | a strong interest in cell biology. |
| From which program/s students can be admitted | Molecular Biology and Genetics |
Deep Learning-Based Next-Generation Wireless Communication Systems
| Faculty Member | Ertugrul Basar |
| Project Name | Deep Learning-Based Next-Generation Wireless Communication Systems |
| Project Description | Assoc. Prof. Ertuğrul Başar’s project titled “Deep learning-based Next-generation Communication Systems” will focus on deep learning-based novel communication system designs and technologies for the 6th generation (6G) and beyond wireless communication networks |
| Project Start Date | 1.08.2022 |
| Project End Date | 1.08.2025 |
| Number of students to be funded | 4 PhD Stundets |
| Desired background & experience | Wireless Communications & Deep/Machine Learning & Physical-Layer Designs |
| From which program/s students can be admitted | Electrical and Electronics Engineering |
CardioStressCI: Prevention of Vascular Cognitive Impairment through Early Detection of Cardiovascular Diseases
| Faculty Member | Attila Gürsoy, Özlem Keskin |
| Project Name | CardioStressCI: Prevention of Vascular Cognitive Impairment through Early Detection of Cardiovascular Diseases |
| Project Description | Cardiovascular disease is a known risk factor for the development of cognitive impairment and dementia as we age, but the reasons for this connection at the level of genes and proteins are still unclear. The goal of CardioStressCI is to identify proteins linked to both disorders, using cellular stress as a guide, with the ultimate goal of informing new diagnostic and treatment methods. |
| Project Start Date | 5.1.2021 |
| Project End Date | 5.1.2024 |
| Number of students to be funded | 1 MSc and/or PhD |
| Desired background & experience | Strong programming skills, preferably familiar with bioinformatics, background in molecular biology (especially protein structure and dynamics) and interested computational biology. |
| From which program/s students can be admitted | Bio-Medical Sciences and Engineering, Computer Science and Engineering, Molecular Biology and Genetics, Computational Sciences and Engineering |
| Additional information | You will be using computational methods developed in COSBILAB to predict how proteins interact with each other, build protein-interaction networks, learn and use network biology algorithms/tools to relate proteins to diseases. |
