PhD position in Physics-Inspired AI for Drug Design
myScience
Basel
Auf einen Blick
- Veröffentlicht:15 Dezember 2025
- Pensum:100%
- Arbeitsort:Basel
Job-Zusammenfassung
PhD-Position in Physik-inspirierter KI für Drug Design an der Universität Basel. Diese Rolle bietet spannende Forschung in einem internationalen Umfeld.
Aufgaben
- Entwicklung neuartiger KI-Algorithmen für die Medikamentenentwicklung.
- Integration physikalischer Prinzipien in neuronale Netzwerke.
- Zusammenarbeit mit experimentellen Forschungsteams zur Validierung.
Fähigkeiten
- MSc in Physik, Computational Chemistry oder Informatik erforderlich.
- Starke Programmierkenntnisse in Python.
- Erfahrung im Bereich Maschinenlernen und neuronale Netze.
Ist das hilfreich?
PhD position in Physics-Inspired AI for Drug Design
Workplace Basel - North West Switzerland - Switzerland CategoryPhD position in Physics-Inspired AI for Drug Design
100% / Available: February 2026
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-ligand 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 position
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-ligand dynamics.You will be responsible for:
- Designing and implementing innovative deep neural network models.
- Integrating physical principles and molecular modeling knowledge into learning architectures.
- Collaborating with experimental research groups, enabling real-world validation and application of newly developed algorithms.
Your profile
- MSc in the fields of Physics, Computational Chemistry or Computer Sciences.
- Excellent knowledge in Statistical Mechanics & Thermodynamics.
- Research experience preferably with publication.
- Strong programming skills in Python.
- 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 candidate 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 ).
Apply
www.unibas.ch')
