Postdoctoral Position in AI-Driven Drug Design

The role is a Postdoctoral Position in AI-Driven Drug Design within the Computational Pharmacy group at the University of Basel. The group focuses on advancing AI approaches in drug design by merging machine learning, physicochemical knowledge, and molecular modeling. The project involves creating an integrated Design, Make, Test, Analyze (DMTA) platform for drug discovery, with a focus on a serine protease from the complement system. The main day-to-day responsibilities include developing and adapting machine-learning approaches for molecular design, integrating physicochemical information into AI workflows, and creating computational workflows for closed-loop DMTA cycles. The role also involves applying and validating these approaches in drug design, collaborating with a diverse team of scientists, and contributing to scientific publications and project reporting. To qualify, candidates must have a PhD in Computational Chemistry, Cheminformatics, Computer Science, Physics, or a related field. Essential skills include a strong background in machine learning and deep learning, proficiency in Python, and experience in at least one of the specified areas: molecular generative AI, cheminformatics, or structure-based drug design. A strong publication record in recognized venues is also required, along with fluent English communication skills. Nice-to-have skills include experience with molecular modeling and a good understanding of the physicochemical principles governing molecular recognition. The role offers an interdisciplinary research environment, close interaction with experimental researchers and industrial partners, and the opportunity to work on cutting-edge AI and drug discovery projects. What the role asks for: - PhD in Computational Chemistry, Cheminformatics, Computer Science, Physics, or a related discipline. - Strong background in machine learning and deep learning. - Strong programming skills, particularly in Python. - Experience in at least one of the following areas: molecular generative AI, cheminformatics and molecular representations, structure-based drug design and protein, ligand modeling. - Strong publication record in internationally recognized, high-quality venues. - Fluent verbal and written communication skills in English.