Post Doc Fellow-Immunogenicity Prediction
MSD
Summary
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.
Job description
Key responsibilities
- 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.
Educational Requirements
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.
Required Experience and Skills
- 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.
Preferred Experience and Skills
- 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 developability, protein engineering, or related optimization concepts.
- Exposure to immunoinformatics, peptide modeling, or related predictive methods is a plus.
Requirements
- 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
Benefits
- Medical insurance
- Dental insurance
- Vision insurance
- Retirement benefits
- 401(k)
- Paid holidays
- Vacation
- Compassionate leave
- Sick days
- Annual bonus
- Long-term incentive
Company overview
MSD, known as Merck Sharp & Dohme outside the United States and Canada, is a global research-intensive biopharmaceutical company headquartered in Rahway, New Jersey. Founded in 1891, the company has established itself as one of the world's leading pharmaceutical corporations with a rich history spanning over 130 years. MSD operates as the international division of Merck & Co., Inc., focusing on the discovery, development, manufacturing, and marketing of innovative medicines, vaccines, biologic therapies, and animal health products. The company's extensive portfolio includes groundbreaking treatments in oncology, infectious diseases, cardiology, diabetes, and vaccines, with notable products such as Keytruda (pembrolizumab) for cancer immunotherapy, Gardasil for HPV prevention, and Januvia for diabetes management. MSD maintains a strong commitment to research and development, investing significantly in scientific innovation to address unmet medical needs worldwide. With approximately 75,000 employees globally, the company operates through four main segments: pharmaceuticals, animal health, healthcare services, and strategic alliances. MSD's mission centers on using leading-edge science to save and improve lives around the world, guided by the principle that medicine is for people, not profits.