PhD student position in AI-based Drug Discovery

The Division of Computational Pharmacy, Department of Pharmaceutical Sciences, is offering a PhD student position to develop and apply cutting-edge computational methodology integrating physicochemical knowledge into deep neural network algorithms for drug discovery applications.
Computational methods have become an important pillar in structure-based drug design for accelerated identification and optimization of therapeutics. In addition to more traditional, physicochemical methods, recent developments in deep learning offer new directions in computational drug discovery. In our research group, we combine the best of both worlds, i.e. data-driven deep neural networks and detailed physicochemical models.
PhD student position is available to extend ongoing research on the development of novel algorithms for drug design combining physics-based modeling with deep neural network concepts. You will be responsible for the development and implementation of novel deep neural network models based on recent developments in normalizing flows, transformer models, graph neural networks, diffusion models and GFlowNets. Existing collaborations with experimental research groups will facilitate the application and testing of novel algorithms in real life settings.

• MSc in the fields of Physics, Physical/Computational Chemistry, Mathematics or Computer Sciences
• Excellent knowledge in Statistical Mechanics & Thermodynamics
• Strong programming skills
• Experience in machine learning, in particular neural network concepts
• Fluent verbal and written communication skills in English
• Highly motivated, interactive team player

• PhD student position
• Training into the key methods of an emerging research field
• International and collaborative research environment

Application / Contact

Please submit your complete application documents, including

• letter (max. 1 page) highlighting motivation, experience and skills
• CV
• Diploma of Bachelor’s and Master’s degree
• contact details of at least two academic references

via the online recruiting platform.

Position is available immediately. You can find out more about us at https://pharma.unibas.ch/de/research/research-groups/computational-pharmacy-2155/ .

For questions, please contact Prof. Markus Lill (markus.lill@unibas.ch).