Job description Objectives: Characterization of the structure-permeability relationship of macrocycles using molecular dynamics simulations and comparison with NMR data Development of novel conformation- and environment-dependent 3D descriptors based on the MD simulations Development and refinement of machine learning models for permeability prediction for macrocycles Secondments planned within the framework of the Doctoral Network : Roche, Switzerland, C. Kroll: Experimental determination of PAMPA permeability coefficients for macrocyclic library U. Uppsala, Sweden, M. Erdelyi: Conformational studies by NMR experiments Bayer, Germany, D. Barber: Conformational sampling of macrocycles Preferred starting date : early. Profile Applicants should hold a M.Sc. in chemistry, computational chemistry, or physics. Experiences with machine learning and/or biomolecular simulation, and strong programming skills (Python) are highly advantageous. Proficiency in English, good communication skills, and social competence are required. We welcome self-motivated candidates with outstanding academic record, excellent communication skills and professional integrity to apply. EU Mobility Rule: Researchers must not have resided or carried out their main activity (work, studies, etc.) in the country of the recruiting beneficiary > 12 months in the 36 months preceding their recruitment date. Workplace Workplace We offer The project offers research and training excellence in medicinal chemistry and drug discovery. The partners of MC4DD are leading research groups in this field and their research institutes actively promote young researchers. The 8 academic research groups and 5 industry partners join forces in