We are looking for a computational/AI biologist who is highly motivated, well organized, and dynamic with a high level of independence and creative thinking but a flexible research approach:
- You need to have a PhD with a topic in one of the following areas: Computational biology, Chemi-informatics, AI-guided antibody design, Insilico drug discovery, In silico protein modelling or similar.
- You should be able to demonstrate one or several of these skills: Implementation and fine-tuning of antibody design models (RFdiffusion and boltzgen, AlphaFold3 etc.). Implementation of affinity prediction and maturation (FoldX, RosettaFold, ESM etc.), virtual screening and developability assessment tools such as aggrescan, AbImmPred etc.
- Strong knowledge of different machine learning architectures and training techniques is essential.
- Proficiency with programming in Python and Bash scripting. - Experience with high performance computing infrastructure and skills in application of statistical approaches to biological problems (e.g. statistical testing, linear modelling).
- A proven and successful publication track record in this field.
- High intrinsic motivation and a strong scientific curiosity.
- Ability to be inventive and to present novel ideas in method development, data analysis and interpretation.
- Team player that can work independently in a multidisciplinary (international) team.
- Excellent oral and written English communication skills.
- Proactive, flexible, and problem-solving attitude.
- Experience in working with deadlines and being involved in multiple projects.
In your role as a computational/AI scientist, you will focus on building advanced antibodies engineering pipeline to derive actionable insight for cancer immunotherapy. Your work will primarily focus on antibody generation, affinity maturation and sequence optimization for target of interest. You will design scalable computational frameworks to generate and evaluate antibody candidates using robust scoring metrics such as binding affinity, developability, and biocompatibility etc. These pipelines will leverage state of the art machine learning models (AlphaFold2, RFdiffusion) and multi-omics data integration to guide the rational design and optimization of therapeutic antibodies.
Overall, you will have the unique opportunity to shape as well and lead a high-impact immuno-oncology relevant computational/AI initiative aiming to mine innovative immunotherapy targets and valorise them toward the clinic. While spearheading this exciting and state-of-the-art research initiative, you will also be expected to have good communication skills, a highly collaborative spirit and willingness to work in collaboration with several different teams and entrepreneurs. You will also be expected to work independently whilst collaborating with multiple teams involved in this platform.
You should be able to organize and troubleshoot your work independently, document it thoroughly and communicate results and experience with the team in a transparent and professional manner.
The team of Prof. Abhishek D Garg (Department of Cellular & Molecular Medicine, KU Leuven) is seeking a suitable postdoctoral-level candidate with specialization in computational biology with emphasis on AI-guided antibody design and/or protein modelling. The team of Prof. Garg aims to apply advanced machine learning and multi-omics approaches in the context of a ground-breaking immuno-oncology “platform”. This role is embedded within an upcoming platform aiming to facilitate successful academia-to-industry valorization trajectories within the context of a collaboration between the teams of Prof. Garg and PharmAbs. For more information, see these websites: https://abhishek-d-garg.wixsite.com/csi-lab, https://lrd.kuleuven.be/pharmabs. The aim of Prof. Garg’s team is to capitalize on high volume cancer patient data and apply cutting edge machine learning and computational biology approaches to shed light on clinically relevant biomarkers and immunotherapeutic pathways. This position will be embedded in the context of intense collaboration between multiple teams, which will provide sufficient growth as well as training opportunities together with high success probability. Selected publications: Kinget, L., Naulaerts, S., Govaerts, J., Vanmeerbeek, I., Sprooten, J., Laureano, R.S., Dubroja, N., Shankar, G., Bosisio, F.M., Roussel, E., Verbiest, A., Finotello, F., Ausserhofer, M., Lambrechts, D., Boeckx, B., Wozniak, A., Boon, L., Kerkhofs, J., Zucman-Rossi, J., Albersen, M., Baldewijns, M., Beuselinck, B., Garg, A.D. (2024). A spatial architecture-embedding HLA signature to predict clinical response to immunotherapy in renal cell carcinoma. NATURE MEDICINE, 30 (6). doi: 10.1038/s41591-024-02978-9 Naulaerts, S., Datsi, A., Borras, D.M., Martinez, A.A., Messiaen, J., Vanmeerbeek, I., Sprooten, J., Laureano, R.S., Govaerts, J., Panovska, D., Derweduwe, M., Sabel, M.C., Rapp, M., Ni, W., Mackay, S., Van Herck, Y., Gelens, L., Venken, T., More, S., Bechter, O., Bergers, G., Liston, A., De Vleeschouwer, S., Van den Eynde, B.J., Lambrechts, D., Verfaillie, M., Bosisio, F., Tejpar, S., Borst, J., Sorg, R., De Smet, F., Garg, A.D. (2023). Multiomics and spatial mapping characterizes human CD8+T cell states in cancer. SCIENCE TRANSLATIONAL MEDICINE, 15 (691), Art.No. ARTN eadd1016. doi: 10.1126/scitranslmed.add1016 Borras, D.M., Verbandt, S., Ausserhofer, M., Sturm, G., Lim, J., Verge, G.A., Vanmeerbeek, I., Laureano, R.S., Govaerts, J., Sprooten, J., Hong, Y., Wall, R., De Hertogh, G., Sagaert, X., Bislenghi, G., D'Hoore, A., Wolthuis, A., Finotello, F., Park, W-Y., Naulaerts, S., Tejpar, S., Garg, A.D. (2023). Single cell dynamics of tumor specificity vs bystander activity in CD8+ T cells define the diverse immune landscapes in colorectal cancer. CELL DISCOVERY, 9 (1), Art.No. ARTN 114. doi: 10.1038/s41421-023-00605-4 Vanmeerbeek, I., Naulaerts, S., Sprooten, J., Laureano, R.S., Govaerts, J., Trotta, R., Pretto, S., Zhao, S., Cafarello, S.T., Verelst, J., Jacquemyn, M., Pociupany, M., Boon, L., Schlenner, S.M., Tejpar, S., Daelemans, D., Mazzone, M., Garg, A.D. (2024). Targeting conserved TIM3+VISTA+ tumor-associated macrophages overcomes resistance to cancer immunotherapy. SCIENCE ADVANCES, 10 (29), Art.No. ARTN eadm8660. doi: 10.1126/sciadv.adm8660
1. Duration: A full-time contract for one year with the possibility to extend (at least) 3 more years thereafter. You are stimulated to apply for a personal postdoctoral funding.
2. The position is immediately available.
3. (Early) access to state-of-the-art as well as novel machine learning infrastructure and methodologies.
4. A stimulating (international, multidisciplinary) research environment where quality, professionalism and team spirit are encouraged.
5. The ability to work on scientifically exceptional and highly valorisable, state-of-the-art initiatives with immediate implications for both industry as well as clinic.
6. The opportunity to be part of a world-class valorization platform (PharmAbs) thereby providing a meaningful contribution to immuno-oncology research.
7. The KU Leuven is one of the most innovative universities in Europe. Leuven is located 20 min. from Brussels, in the centre of Europe.