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Postdoctoral Fellow
Postdoctoral Fellow: Computational Protein Design and Machine Learning Full-time NEW
Postdoctoral Fellow: Computational Protein Design and Machine Learning
The Division of Pulmonary, Critical Care and Sleep Medicine at the University of Maryland, Baltimore, School of Medicine is seeking a postdoctoral fellow with expertise in computational protein design, structural bioinformatics, or machine learning for molecular engineering. The fellow will work closely with experimental researchers to design and optimize therapeutic proteins, nanobodies, receptor-binding molecules, and imaging agents.
Key Responsibilities
• Apply protein structure prediction and computational design methods to generate and prioritize candidate molecules.
• Analyze protein–protein interactions and sequence–structure–function relationships.
• Integrate relevant biological datasets to inform target selection and molecular design requirements.
• Collaborate closely with experimental team members so that computational predictions are tested and experimental data feed directly back into design.
• Contribute computational and analytical support across the lab’s broader research program as needed.
• Present findings at internal meetings and scientific conferences and prepare manuscripts for highimpact journals.
• Mentor junior researchers and contribute to a collaborative lab environment.
Qualifications
• PhD in Computational Biology, Bioinformatics, Biophysics, Chemistry, Computer Science, or a related field.
• Demonstrated experience with modern protein structure prediction and design tools, including AlphaFold2/AlphaFold3, Boltz, or Chai-1 for structure and complex prediction; RFdiffusion, ProteinMPNN, or Rosetta for design and inverse folding; and ESM-family or other protein language models.
• Proficiency in Python and scientific computing is expected.
• Experience with molecular dynamics simulation (GROMACS, OpenMM, or AMBER) and structural analysis of protein-ligand interactions.
• Experience analyzing deep mutational scanning, directed evolution, or other high-throughput sequencing datasets is desirable.
• A track record of computational designs that were experimentally validated, or prior experience working alongside a wet lab, is a strong plus.
• Ability to work independently and within a collaborative team; strong communication and writing skills.
Application: Interested candidates should send their CV, a cover letter outlining their research experience, interest in this position and contact information for three references to Dr. Nalinikanth Kotatgiri at [Please click the Apply button for the link or address]