Postdoc Fellow — AI-Driven Immunogenicity Predictor

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

Benefits offered by this job

Annual bonus

Long-term incentive

Medical, dental, vision benefits

Job summary

HireLifeScience is seeking a Postdoctoral Fellow in the Computational Toxicology group within Nonclinical Drug Safety to advance AI/ML and structure-based approaches for immunogenicity prediction in biologic and peptide therapeutics. The role emphasizes publication, collaboration, and development of reusable computational capabilities for translational safety assessment.

The fellow will curate diverse datasets, develop integrated predictive models, publish first-author papers, present findings

Qualifications

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

Responsibilities

• Curate and harmonize biological, structural, and clinical data into reproducible analysis workflows.
• Develop immunogenicity prediction methods by integrating sequence, structure, and experimental data.
• Apply structure-based modeling and simulation to characterize biomolecular interactions relevant to immune recognition.
• Build and benchmark multimodal ML models combining sequence, structure, and tabular data.
• Evaluate model performance and generalizability across modalities.
• Contribute to first-author manuscripts and scientific presentations.
• Collaborate across computational, biology, and experimental teams and deliver reproducible code and models.

Skills

AlphaFold2/Boltz2

Molecular dynamics

Python

PyTorch

HPC/GPU

Immunology knowledge

Scientific communication

Education

PhD in computational immunology/biology/chemistry/structural biology/bioinformatics

PhD due by Spring 2027

Tools

Python

PyTorch

Job description

HireLifeScience is seeking a Postdoctoral Fellow in the Computational Toxicology group within Nonclinical Drug Safety to advance AI/ML and structure-based approaches for immunogenicity prediction in biologic and peptide therapeutics. The role emphasizes publication, collaboration, and development of reusable computational capabilities for translational safety assessment.

The fellow will curate diverse datasets, develop integrated predictive models, publish first-author papers, present findings