Postdoctoral Researcher - Biomolecular AI & Protein-Ligand / Protein-membrane interaction modeling

Requirements

• PhD (or equivalent experience) in Computational Biology, Structural Biology, Biophysics, Physics, Statistical Mechanics, or a related field.
• Expertise in biomolecular simulation, including molecular dynamics system setup, force field selection, simulation parameterization, and related methodologies.
• Deep theoretical and practical experience with free energy perturbation (FEP), alchemical free energy methods, and assessment of convergence, uncertainty, and simulation quality.
• Experience developing scientific software and computational workflows in Python.
• Demonstrated ability to lead research projects independently and collaborate effectively across multidisciplinary teams.

Nice to Haves

• Experience with membrane modeling, including lipid composition selection, equilibration, and simulation setup.
• Experience integrating structural biology or molecular simulation data with machine learning approaches.
• Experience generating, curating, or managing large-scale scientific datasets.
• Experience collaborating with machine learning researchers on data generation or model development.

What You'll Be Doing

• Design, execute, and analyze large-scale molecular dynamics (MD) simulations, including protein-ligand, protein-membrane, and free energy perturbation (FEP) studies.
• Develop high-quality, machine learning-ready datasets by translating scientific questions into scalable data generation campaigns and defining appropriate quality standards and evaluation metrics.
• Build reproducible computational workflows and automation pipelines using Python and distributed computing infrastructure.
• Collaborate with machine learning researchers, computational biologists, and research engineers to integrate simulation outputs with advanced modeling workflows.
• Design and implement model-informed experimentation strategies leveraging simulation results to improve biological modeling and predictive performance.

Perks and Benefits

• Collaborative research environment that bridges ML with meaningful biomedical impact.
• Opportunity to contribute to state-of-the-art ML methods in drug discovery.
• Contribute novel methods, publications, datasets, software, and research direction.
• Equal opportunity employer with various support and accommodation options for applicants.