Proteins play a crucial role as mediators of the therapeutic potential of molecules. Capturing meaningful information on proteins with AI has an enormous potential in drug discovery and chemical biology, e.g., for structure-based drug discovery and polypharmacology. Despite such potential, strategies to capture sophisticated information on protein structure with AI are underexplored compared to small molecules.

This ERC-funded project has the ambitious goal to develop new AI strategies to learn efficiently from protein structures, to accelerate small molecule drug discovery. The project will be fueled by methodological innovation and aimed to leverage large corpora of protein data with cutting-edge deep learning algorithms. The developed approaches will be applied experimentally for structure-based drug discovery, thereby providing a unique opportunity to validate the AI predictions in a real-world setting.

Job Description

Your tasks will include:

• Developing and implementing innovative algorithms to capture sophisticated structural information for structure-based drug discovery with AI.
• Implementing cutting-edge deep learning approaches to efficiently learn from large corpora of protein structures.
• Collaborating and interacting with ongoing research in ligand-based AI, as well as in medicinal chemistry and chemical biology.
• Mentoring and supervising junior researchers and students who are working on AI-assisted drug discovery.
• Communicating the results of your research through publications in scientific journals and presentations at conferences.

You will work at the interface between AI, chemistry, and biology, with a proactive and interdisciplinary attitude. You will become a member of the Molecular Machine Learning team (led by Dr. F. Grisoni), whose mission is to augment human intelligence in drug discovery with novel AI technology. You will also be embedded in the Chemical Biology group, the Dept. of Biomedical Engineering, the Institute for Complex Molecular Systems, and the Eindhoven AI Systems Institute, which are characterized by a highly interdisciplinary and collaborative approach to science and research.

Background:

• A PhD degree (or an equivalent university degree) in Bioinformatics, Computer Sciences, or related disciplines.
• Advanced understanding of molecular biology and/or medicinal chemistry.

Technical skills:

• Excellent knowledge of Python (required).
• Previous experience with deep learning (required).
• Knowledge of popular deep learning frameworks such as Tensorflow or PyTorch (required).
• Experience with popular bioinformatics software, such as PyMol, and/or Chimera (desirable).
• Familiarity with Unix/Unix-like operating systems (desirable).

Soft skills:

• A research oriented and quantitative thinking attitude.
• Proven ability to work in interdisciplinary teams.
• Willingness to support and mentor younger scientists working in AI for drug discovery.
• Excellent writing and presentation skills.
• Fluent in spoken and written English (C1 level).

A meaningful job in a dynamic and ambitious university, in an interdisciplinary setting and within an international network. You will work on a beautiful, green campus within walking distance of the central train station. In addition, we offer you:

• Full-time employment for 3 years.
• Salary in accordance with the Collective Labour Agreement for Dutch Universities, scale 10 (min. € 3.557,- max. € 4.670,-).
• A year-end bonus of 8.3% and annual vacation pay of 8%.
• High-quality training programs on general skills, didactics and topics related to research and valorization.
• An excellent technical infrastructure, on-campus children's day care and sports facilities.
• Partially paid parental leave and an allowance for commuting, working from home and internet costs.
• A TU/e Postdoc Association that helps you to build a stronger and broader academic and personal network, and offers tailored support, training and workshops.
• A Staff Immigration Team is available for international candidates, as are a tax compensation scheme (the 30% facility) and a compensation for moving expenses.