100% / Available: upon agreement
Neural network models have transformed many areas of life sciences, including protein structure prediction and molecular generation. However, due to limited high-quality data, purely data-driven AI models often lack the generalizability required to reliably model protein鈥搇igand interactions, as recently demonstrated by our group ( https://doi.org/10.1038/s41467-025-63947-5 ).
Our research therefore focuses on advancing next-generation drug design methodologies by integrating physicochemical principles directly into deep neural network approaches. Representative publications from our group include:
https://doi.org/10.1021/acs.jcim.2c01436
https://doi.org/10.1021/acs.jcim.1c01438
https://icml-compbio.github.io/2023/papers/WCBICML2023_paper159.pdf
https://doi.org/10.1038/s42004-020-0261-x
Your assignments
A fully funded PhD position is available in the Computational Pharmacy group at the University of Basel. The successful candidate will contribute to ongoing research on the development of novel physics-guided AI algorithms for drug design, integrating physics-based modeling with state-of-the-art deep learning methods. The project will focus on creating a next-generation docking framework that explicitly incorporates protein鈥搇igand dynamics.
You will be responsible for:
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Designing and implementing innovative deep neural network models.
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Integrating physical principles and molecular modeling knowledge into learning architectures.
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Collaborating with experimental research groups, enabling real-world validation and application of newly developed algorithms.
Your profile
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MSc in the fields of Physics, Computational Chemistry or Computer Sciences.
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Excellent knowledge in Statistical Mechanics & Thermodynamics.
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Research experience preferably with publication.
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Strong programming skills in Python.
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Experience in machine learning, in particular neural network concepts.
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Fluent verbal and written communication skills in English.
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Highly motivated, interactive team player.
We offer you
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Application / Contact**
Please submit your complete application documents, including
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Letter (max. 1 page) highlighting motivation, experience and skills
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CV
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Diploma of Bachelor鈥檚 and Master鈥檚 degree
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Contact details of at least two academic references聽
via the online recruiting platform.
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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).