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 cutting-edge computational methodology and algorithms combining deep neural networks and detailed physicochemical modeling 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.

Your position

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 or graph-based neural networks. Existing collaborations with experimental research groups will facilitate the application and testing of novel algorithms in real life settings.

Your profile

• MSc in the fields of Computer Sciences, Mathematics, Physics or related disciplines.
• 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.

We offer you

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

• 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