Postdoc Fellow: Immunogenicity AI/Structure Modeling

MSD; West Point (PA); On-site; USD 82,000 - 92,000; Full time

Benefits offered by this job

Annual bonus

Long-term incentive

Comprehensive benefits package

Job summary

MSD seeks a Postdoctoral Fellow to advance AI/ML and structure-based approaches for immunogenicity prediction in biologics and peptides. The role blends protein structure modeling, immunoinformatics, and multimodal machine learning, in a publication-oriented two-year fellowship with strong mentorship and cross-functional collaboration.

The fellow will publish first-author papers, present at conferences, and develop reusable computational capabilities for translational safety assessment, while

Qualifications

• PhD in computational immunology, computational biology, computational chemistry, structural biology, bioinformatics, or closely related field.
• Strong hands-on experience with structure-based modeling and AI/ML methods.
• Excellent scientific communication and first-author publication record.

Responsibilities

• Curate and harmonize datasets for reproducible analysis workflows.
• Develop immunogenicity prediction methods integrating sequence and structure data.
• Apply structure-based modeling and multimodal ML to predict immune responses.
• Build and benchmark models combining sequence, structure, and tabular data.
• Collaborate cross-functionally and publish first-author manuscripts.

Skills

Python

PyTorch

Immunology knowledge

HPC / GPU

Scientific communication

Education

PhD in computational immunology or related field

Tools

AlphaFold2 / Boltz2

Molecular Dynamics

Git

Job description

MSD seeks a Postdoctoral Fellow to advance AI/ML and structure-based approaches for immunogenicity prediction in biologics and peptides. The role blends protein structure modeling, immunoinformatics, and multimodal machine learning, in a publication-oriented two-year fellowship with strong mentorship and cross-functional collaboration.

The fellow will publish first-author papers, present at conferences, and develop reusable computational capabilities for translational safety assessment, while