Associate Scientist, Post Doc Fellow- AI/ML & Computational Biology for Antigen Design

• Location: West Point, PA (On-site)
• Department: Infectious Diseases & Vaccines Discovery / Data Science & Scientific Informatics

We are seeking an exceptional and motivated Postdoctoral Scientist to join our interdisciplinary team at the forefront of computational vaccine discovery. In this role, you will pioneer and apply state-of-the-art artificial intelligence (AI), machine learning (ML), and computational protein design techniques to solve fundamental challenges in structure-based antigen design.

Many viral pathogens present dynamic, conformationally flexible surface proteins that require precise structural stabilization to elicit potent protective immune responses. You will spearhead the development of predictive AI/ML workflows to model, design, and optimize conformationally constrained viral antigens. Working closely with both computational biologists and high-throughput experimental screening groups, you will operate in a tight computational-experimental feedback loop to iteratively fine-tune generative models with empirical datasets.

• →Algorithm & Workflow Development: Develop, benchmark, and deploy novel AI/ML architectures including protein language models, folding models, geometric deep learning models, and inverse-folding models for variant prediction and conformational stabilization.
• →Physics & Machine Learning Integration: Explore hybrid computational strategies integrating molecular dynamics (MD) simulations, energetic modeling, and deep learning representations to capture dynamic conformational transitions.
• →Data-Driven Iteration: Interface with high-throughput automated expression, biophysical characterization, and analytical screening platforms to iteratively benchmark, retrain, and refine candidate models using prospective experimental data.
• →Scientific Dissemination: Author high-impact manuscripts, present findings at prestigious computational biology and vaccine development symposiums, and champion open science practices within industry boundaries.
• →Cross-Functional Collaboration: Partner with subject-matter experts across structural biology, virology, immunology, and research computing to translate computational breakthroughs into translatable vaccine designs.

• Must currently hold a PhD OR
• Receive a Ph.D. no later than spring 2027
• Ph.D. in Computational Biology, Bioinformatics, Computer Science, Biophysics, Computational Chemistry, or a related quantitative discipline.

• AI & Machine Learning Expertise: Demonstrated experience developing or applying machine learning models to biomolecular data, with practical fluency in protein language models (e.g., ESM), folding models (e.g., AlphaFold), or structural inverse-folding (e.g., ProteinMPNN) paradigms.
• Computational Fluency: Strong proficiency in Python and modern ML frameworks (e.g., PyTorch, JAX), alongside experience in high-performance computing (HPC), GPU workflows, version control, and reproducible software development.
• Publication Track Record: Proven record of independent research demonstrated through first-author peer-reviewed publications, preprints, or presentations at major conferences.

• Experience with structural modeling suites (e.g., ColabFold, Rosetta, PyMOL/ChimeraX) and molecular dynamics tools (e.g., AMBER, Desmond, GROMACS, OpenMM).
• Working knowledge of biophysical and biochemical characterization methods (e.g., thermal stability/nanoDSF, binding kinetics/SPR/BLI, SEC) and how experimental data informs model training.
• Experience with dynamic or oligomeric protein complexes which undergo conformational rearrangements.
• Strong written and verbal communication skills with an appetite for working in multidisciplinary, team-oriented environments.

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