Post Doc Fellow-Immunogenicity Prediction

The Computational Toxicology group within Nonclinical Drug Safety (NDS) seeks a Postdoctoral Fellow to advance AI/ML and structure-based approaches for immunogenicity prediction in biologic and peptide therapeutics. The successful candidate will develop next-generation computational methods combining protein structure modeling, immunoinformatics, and multimodal machine learning to improve prediction of biologically relevant immune responses. This is a publication-oriented, two-year fellowship with strong mentorship and extensive cross-functional and external academic collaboration.

The Fellow will publish first-author manuscripts, present at scientific conferences, and help establish reusable, well-documented computational capabilities for translational safety assessment.

• Curate, integrate, and harmonize biological, structural, and clinical datasets from public and internal sources into reproducible analysis workflows.
• Develop computational methods for immunogenicity prediction by integrating sequence, structural, and experimental data.
• Apply structure-based modeling and simulation approaches to characterize biomolecular interactions and conformational behavior relevant to immune recognition.
• Build and benchmark multimodal machine-learning models that combine sequence, structural, and tabular data into robust, calibrated predictors.
• Evaluate model performance against established approaches and assess generalizability across therapeutic modalities.
• Contribute to first-author manuscripts and scientific presentations at internal forums and external conferences.
• Collaborate with computational, biology, and experimental scientists, and deliver reproducible, well-documented code, analyses, and models.

• Must currently hold a PhD OR
• Receive a Ph.D. no later than spring 2027
• Ph.D. in computational immunology, computational biology, computational chemistry, structural biology, bioinformatics, or a closely related field.

• Strong hands-on experience with co-folding models (e.g AlphaFold2/Boltz2), multimer modeling, or related structure-based computational methods.
• Experience with molecular dynamics simulation and analysis of biomolecular systems, complexes, or conformational behavior.
• Strong software development skills in Python and modern deep-learning frameworks such as PyTorch, with sound practices in version control, reproducibility, and scientific computing.
• Working knowledge of immunology relevant to immunogenicity prediction, including antigen processing and presentation, MHC / HLA biology, CD4⁺ T-cell recognition, and anti-drug antibody (ADA) responses.
• Comfort working in HPC / GPU and cloud computing environments.
• Excellent scientific communication skills and a strong first-author publication record.

• Experience applying computational methods to immunology, structural biology, or protein science problems.
• Familiarity with antigen processing and presentation, MHC/HLA biology, or related immune recognition concepts.
• Experience with data integration, biological dataset curation, and programmatic workflows using public resources and APIs.
• Familiarity with uncertainty quantification, model calibration, and rigorous benchmarking.
• Familiarity with biotherapeutic develop ability, protein engineering, or related optimization concepts.
• Exposure to immunoinformatics, peptide modeling, or related predictive methods is a plus.

The salary range for this role is: $82,000- $92,000

• annual bonus and long-term incentive, if applicable.
• We offer a comprehensive package of benefits.
• Available benefits include medical, dental, vision healthcare and other insurance benefits (for employee and family), retirement…