ppArtificial intelligence is rapidly transforming molecular design and drug discovery. However, the identification of successful drug candidates requires more than generating molecules with high predicted affinity: selectivity, physicochemical properties, potential adverse effects, synthetic accessibility, and experimental feedback must be considered simultaneously. /p pOur research in the Computational Pharmacy group at the University of Basel focuses on developing next-generation AI approaches for drug design by combining state-of-the-art machine learning with physicochemical knowledge and molecular modeling. Representative publications from our group include: /p h3bYour position /b /h3 pA fully funded Postdoctoral position is available in the Computational Pharmacy group at the University of Basel within an international Innosuisse research project on AI-driven closed-loop drug discovery. The project aims to establish an integrated Design-Make-Test-Analyze (DMTA) platform combining generative AI, ultra-large synthetically accessible chemical spaces, physics-informed molecular representations, off-target prediction, and experimental feedback. The developed methods will be applied in iterative prospective drug-discovery cycles, with a serine protease from the complement system serving as a real-world lead-optimization case study. The successful candidate will play a central role in the computational and AI components of the project and work closely with our international and industrial project partners. /p pYou will be responsible for: /p ulliDeveloping and adapting machine-learning approaches for structure-based and generative molecular design. /liliIntegrating physicochemical information, including protein-ligand interaction features, into generative AI workflows. /liliDeveloping computational workflows for closed-loop DMTA cycles in which experimental affinity, selectivity, and molecular-property data are continuously used to improve the next generation of proposed molecules. /liliApplying and validating the developed approaches prospectively in the design and optimization of serine protease inhibitors. /liliCollaborating closely with computational scientists, chemists, and biologists within the international project consortium. /liliContributing to scientific publications, presentations, and project reporting. /li /ul h3bYour profile /b /h3 ulliPhD in Computational Chemistry, Cheminformatics, Computer Science, Physics, or a related discipline. /liliStrong background in machine learning and deep learning. /liliStrong programming skills, particularly in Python. /liliExperience in at least one of the following areas: /lilimolecular generative AI, /lilicheminformatics and molecular representations, /lilistructure-based drug design and protein-ligand modeling, /liliExperience with molecular modeling and a good understanding of the physicochemical principles governing molecular recognition is highly desirable. /liliA strong publication record in internationally recognized, high-quality venues is required, such as leading journals in computational chemistry (e.g., JCTC, Journal of Chemical Physics) or top-tier machine-learning conferences (e.g., ICLR, ICML, NeurIPS), as appropriate to the candidate's research background. /liliFluent verbal and written communication skills in English. /liliHighly motivated, independent, and collaborative researcher with an interest in working at the interface between methodological development and prospective drug discovery. /li /ul h3bWe offer you /b /h3 ulliA Postdoctoral position in an interdisciplinary research project at the interface of artificial intelligence and drug discovery. /liliThe opportunity to develop new computational methodologies and directly test them in prospective Design-Make-Test cycles. /liliClose interaction with experimental drug-discovery researchers and industrial and international project partners. /liliAn international and collaborative research environment at the University of Basel. /li /ul pYou can find out more about our research at: /p h3bFor questions, please contact Prof. Markus Lill ( ). /b /h3 /p #J-18808-Ljbffr
Postdoctoral Position in AI-Driven Drug Design Arbeitgeber: Universität Basel
Die Universität Basel ist ein hervorragender Arbeitgeber, der seinen Mitarbeitenden eine langfristige Perspektive in einem stabilen und zukunftsorientierten Umfeld bietet. Mit einer sorgfältigen Einarbeitungsphase und kontinuierlichen Weiterbildungsmöglichkeiten fördert die Universität aktiv das Wachstum ihrer Angestellten. Die offene und serviceorientierte Arbeitskultur ermöglicht es Ihnen, direkt mit Dozierenden und Veranstaltenden zusammenzuarbeiten und Ihre technischen Fähigkeiten in einem internationalen und dynamischen Team weiterzuentwickeln.
