Innovation Postdoctoral Fellow, AI-ML for Targeted Protein Degradation

Band

Level 3

 

Job Description Summary

We are thrilled to seek applications for our Data Science Innovation Fellowship track of the Novartis Postdoctoral Fellowship Program.

This applied research program is designed to change the way we approach drug discovery, offering fellows a unique chance to train in Data Science and AI for biomedical research. As a fellow, you will learn to apply your quantitative and computational skills to make a difference for patients and reimagine medicine at Novartis.

As part of the Data Science Innovation Fellowship track of the Novartis Postdoctoral Fellowship Program, you will join our vibrant, dedicated postdoctoral community for events, including the monthly postdoc seminars and other scheduled events for postdocs. Fellows are surrounded by a supportive, collaborative community of postdocs and scientists, who would contribute to the acceleration of your scientific growth, along with building your professional skillset, e.g. you will have the opportunity to do a Postdoc Practicum in another laboratory or in a business function of Novartis. This applied research program is up to 3 years in length, with the option of applying for an extension of up to 1 year (pending review by the Head of Biomedical Education & Innovation and the postdoc supervisor).

Drug hunting is a team sport, and you will gain experience in Data Science & AI for drug discovery as part of a multi-disciplinary team in Biomedical Research. You will drive innovation by deploying cutting-edge data approaches in collaboration with a vibrant and diverse community of over 300 data scientists globally. The program provides a unique platform to work on real-world, biomedical data at scale, rarely accessible in academia. Seize this chance to be at the forefront of Data Science and AI, and shape the future of drug discovery!

 

Job Description

Internal Job Title: Innovation Postdoctoral Fellow

Position Location: onsite, Cambridge, MA #LI-onsite

About the Role:

Are you passionate about leveraging cutting-edge AI and machine learning (ML) techniques to revolutionize the field of targeted protein degradation (TPD)? Novartis, a pioneer in this space, is seeking an exceptional Data Science Postdoc with a focus on advancing computational methods for drug discovery. 

Our unique research program integrates advanced computational tools with very large and proprietary internal datasets that integrates deep CRBN glue SAR with functional data across hundreds of previously undrugged targets and includes extensive ground-truth ternary complex X-ray and cryoEM structures with whole cell proteomics results. Together these data provide a unique opportunity to model biological interactions, exploring novel chemical and target spaces. You will collaborate with interdisciplinary experts across data science, cheminformatics, structural biology, and biology to develop transformative solutions that impact diverse therapeutic areas.

Start date: October 2025

Key Responsibilities:

• Develop ML strategies to leverage internal TPD recruitment and degradation data for improved chemotype diversity, hit rates, and understanding of canonical and non-canonical glues. 

• Explore diverse glue modes through advanced computational pipelines and apply generative AI models to design novel CRBN binding warheads and. 

• Analyze protein-protein interactions (PPIs) and target-E3 ligase-glue interactions using tools like co-folding techniques, protein surface interaction predictors, and advanced 3D GenChem approaches. 

• Collaborate with internal and external experts to implement AI-driven workflows for identifying binding pockets and designing novel ligand structures. 

• Validate algorithmic outputs through retrospective benchmarking and experimental collaboration for biological validation. 

• Support pipeline development for structural design of TPD candidates and contribute to impactful publications and presentations in leading scientific journals. 

Key Requirements:

• Ph.D. (awarded within the past 2 years) in Computer Science, Machine Learning, Computational Chemistry, Biophysics, or related fields. 

• Proficiency with scientific programming languages (e.g., Python) as well as strong skills in algorithm development, data analysis, and experience with AI/ML frameworks (e.g., PyTorch, JAX) for drug discovery, protein modeling, or cheminformatics. 

• Familiarity with 3D generative chemistry methods (e.g., VAEs, flow and diffusion models, transformers), protein surface analysis tools, or related TPD research techniques. 

• Experience manipulating large, complex data sets.