Doctoral fellow Department of Information Technology | |
Published | |
Workplace | Flemish Region, Ghent, Belgium |
Category | |
Position | |
Last application date Dec 31, 2024 00:00 Department TW05 - Department of Information Technology Contract Limited duration Degree Master’s degree in computer science, artificial intelligence, bioinformatics, mathematics, or equivalent Occupancy rate 100% Vacancy type Research staff
Job descriptionPhD Researchers on AI for drug design Job description ABOUT GHENT UNIVERSITY Ghent University is a world of its own. Employing more than 15.000 people, it is actively involved in education and research, management and administration, as well as technical and social service provision on a daily basis. It is one of the largest, most exciting employers in the area and offers great career opportunities.With its 11 faculties and more than 85 departments offering state-of-the-art study programmes grounded in research in a wide range of academic fields, Ghent University is a logical choice for its staff and students. ABOUT IDLAB The IDLab Ghent research group from Ghent University - imec in Belgium is extending its interdisciplinary research cluster focusing on artificial intelligence for bio-informatics, within a new collaboration project between academia and industry, focusing on the AI-driven generation and optimization of proteins for therapeutic purposes. This vacancy encompasses 4 different PhD tracks within the same project, focusing on developing machine learning models and training strategies for the creation and adaptation of antibodies and nanobodies, with the following topics of focus: - Topic 1: designing prediction models for the developability properties thermostability, spatial aggregation propensity, and sequence liabilities (with a strong focus on the application level), - Topic 2: designing multi-objective guidance strategies of protein diffusion models for developability-aware generation (both fundamental and application oriented), - Topic 3: design and training of energy-based models for unsupervised binding affinity prediction (mostly fundamental AI research), - Topic 4: energy-based representation learning techniques for small molecules as drug candidates (more fundamental AI research). We are seeking highly motivated and talented PhD candidates to join the AI-bioinformatics research cluster, with profs. Thomas Demeester, Jan Fostier, and Kathleen Marchal, combining their respective expertise in machine learning, high-performance compute in bioinformatics, and AI for biology. The PhD fits within the recently launched imec Health Mission, a strategic research track aiming to advance the discovery and development of novel therapeutics over the next 10 years. IDLab Ghent is a key partner in the Health Mission, and leads the first mission project ADAPT (for "Affinity and Developability through AI for Protein Therapeutics"). ADAPT aims to develop AI-based models to design and optimize antibodies and nanobodies with high binding affinity and optimal developability properties, in a collaboration with several imec research groups and key industrial partners from the Ghent area specialized in nanobody technology. The main tasks for the PhD candidates will include: o Studying state-of-the-art techniques and benchmarks in the domain of antibody design with machine learning models (including protein language models and/or protein diffusion models). o Design, implementation, and training of new models, focusing on one of the topics listed above. o Collaboration towards proof-of-concept implementations of the created models for validation by experts in the domain of antibody engineering. o Writing high quality publications, targeting top journals and international conferences. In addition to these primary research responsibilities, the PhD candidate will actively contribute to the educational mission of our institution by providing (limited) support for courses in the area of AI and/or bioinformatics, and taking on a mentoring role by supervising master theses related to the subject of the PhD. | |
In your application, please refer to myScience.org and reference JobID 2934336. |
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