This PhD project at the Max Planck Institute for Molecular Genetics focuses on the computational and evolutionary design of multi-target peptide inhibitors for respiratory viruses. The research group specializes in computational drug design, virtual screening, and in silico methods to explore ligand-protein interactions, with a strong track record in antivirals and enzyme inhibition. The project aims to develop evolutionary algorithms for peptide design and optimization, targeting multiple viral proteins responsible for common respiratory infections. Students will engage in structure-based analysis of mutational and structural data, and develop a scoring function for peptide activity, which will be iteratively validated through synthesis and experimental testing.

The interdisciplinary environment offers collaboration with experts in computational drug design and experimental research, access to state-of-the-art computational resources, and opportunities for career development through conferences, workshops, and international collaborations. Applicants should have a Master’s degree in Pharmacy, Biochemistry, Chemistry, or a related field, with excellent grades and expertise in molecular modeling, chemistry, and machine learning.

Experience with Python, TensorFlow, PyTorch, and virtual screening techniques is highly valued. The position is fully funded, and the application deadline is January 7, 2026.