Post Doc Fellow-Immunogenicity Prediction
Merck
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.
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
- 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, Dental & Vision
- Retirement Benefits
- 401(k)
- Paid Holidays
- Vacation
- Compassionate Leave
- Sick Days
- Annual Bonus
- Long-Term Incentive
Company overview
Merck & Co., Inc., known as Merck in the United States and Canada and MSD (Merck Sharp & Dohme) internationally, is a premier American multinational pharmaceutical company headquartered in Rahway, New Jersey. Founded in 1891 as the U.S. subsidiary of the German Merck Group, the company became independent following World War I and has since grown into one of the world's largest biomedical companies, ranking fourth globally by revenue. Merck operates across three core business segments: pharmaceuticals, vaccines, and animal health products. The company is renowned for its groundbreaking research and development, having brought numerous life-saving medicines to market including Keytruda (pembrolizumab), a leading immunotherapy for cancer treatment that generated $29.5 billion in 2024 revenue, and Gardasil, a revolutionary HPV vaccine. With approximately 75,000 employees worldwide, Merck maintains a robust pipeline of innovative therapies focused on oncology, vaccines, hospital acute care, cardiology, neuroscience, virology, immunology, and diabetes. The company invests heavily in R&D, spending $17.9 billion in 2024 alone, demonstrating its commitment to advancing scientific discovery. Merck's legacy spans over 130 years of bringing hope to humanity through important medicines and vaccines, operating with a purpose to use leading-edge science to save and improve lives globally while fostering an inclusive workforce and responsible business practices.