Post Doc Fellow-Immunogenicity Prediction

Role: Postdoctoral Fellow in Computational Toxicology focused on advancing AI/ML and structure-based methods for immunogenicity prediction in biologic and peptide therapeutics. Primary objectives include developing and benchmarking multimodal machine learning models integrating sequence, structural, and experimental data to improve immune response predictions. Responsibilities encompass data curation, protein structure modeling (e.g., AlphaFold2), biomolecular interaction analysis, and delivering reproducible computational workflows. The position emphasizes publication, scientific presentation, and cross-disciplinary collaboration. Qualifications: PhD in computational immunology, biology, chemistry, structural biology, or related fields, or conferred by Spring 2027. Essential skills include experience with structure-based modeling (e.g., multimer modeling, molecular dynamics), proficiency in Python and deep learning frameworks (PyTorch), and knowledge of immunology fundamentals such as MHC/HLA biology and immunogenicity mechanisms. Preferred skills involve applying computational methods to immunology, familiarity with biotherapeutic developability, and data integration expertise. High-value aspects include focus on biologics and peptides, immunogenicity prediction, structure-based modeling, and a publication-oriented fellowship. The role involves collaborative work within a biopharma setting, with an expected 10% travel, and is based in a hybrid or flexible arrangement, with a salary range of $82,000-$92,000. The fellowship is a two-year, mentorship-rich program aimed at establishing computational capabilities for translational safety assessment.