Computational Antibody Design, Computational Discovery Co-Op

Job Summary
Computational Discovery is seeking an independent and self-motivated Co-Op student to contribute to the advancement of AI/ML-guided drug discovery programs. The successful candidate will contribute to protein design and engineering—with antibody-focused applications such as paratope/epitope modeling—by applying statistical and computational techniques to real drug discovery design campaigns. They will assist in developing computational workflows that leverage large scale antibody sequence databases together with de novo antibody design strategies for early hit identification. This position provides exceptional learning experience in AI/ML-guided biologics design, offering experience in practical ML applications for drug discovery, literacy in structure prediction methods and experience in a fast-paced, cross-functional Biopharma team.

Key responsibilities

• Develop & deploy machine learning, deep learning, and/or physics-based models for hit identification and generative antibody design.
• Apply large-scale data mining and structural modeling to discover and refine antibody sequence-function relationships.
• Build, train, and fine-tune AI/ML models (e.g., linear/logistic regression, gradient-boosted and other multi-parametric models, and transformer-based protein language models
• Translate designs into testable constructs and integrate multi-dimensional assay readouts to close the design → make → test → retrain loop 
• Work cross-functionally with wet-lab teams to enable lab-in-the-loop driven drug discovery
• Contribute to data visualization, scientific documentation, and presentation of computational results to an inter-disciplinary team.

Minimum Qualifications

• Ph.D. candidate in Bioinformatics, Computational Biology, Data Science, Biostatistics or similar.
• Proficiency in Linux, Python and/or Rstudio relevant ML tools such as git, Jupyter Notebooks Scikit-Learn etc.
• Experience in leveraging statistical and machine learning approach on large datasets
• Excellent verbal and written communication skills.

Preferred Qualifications

• Experience utilizing High Performance computing (HPC) and AWS cloud computing infrastructure.
• Proficiency in Deep Learning frameworks (eg:  PyTorch, Tensorflow/Keras)
• Working knowledge of public antibody and protein data resources, such as SAbDab, Thera-SAbDab, OAS (Observed Antibody Space), IMGT, PDB, AlphaFold DB, and UniProt, and ability to extract, preprocess, and curate data for model training
• Experience in state-of-the-art applications of ML on antibody design (eg: protein language models, folding model, Structure based models)

Disclaimer: The above statements are intended to describe the general nature and level of work performed by employees assigned to this job. They are not intended to be an exhaustive list of all duties, responsibilities, and qualifications.  Management reserves the right to change or modify such duties as required.

Incyte Corporation is committed to creating a diverse environment and is proud to be an equal opportunity employer.

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