Data Science & AI Innovation Postdoctoral Fellow

Novartis•Cambridge, MA

3d•Onsite

About The Position

We are excited to invite applications for the Novartis Biomedical Research Postdoctoral Fellowship Program; a unique training opportunity designed for exceptional early-career scientists eager to tackle some of the most challenging problems in biomedical research and drug discovery. As a Postdoctoral Research Fellow, you will join the Cardiovascular and Metabolism disease area in Cambridge and pursue an innovative research project at the forefront of biomedical science and drug discovery. You will work alongside leading scientists in a highly collaborative, multidisciplinary environment while gaining exposure to the broader ecosystem that translates scientific discovery into medicines. Our fellows are empowered to ask bold scientific questions, apply cutting-edge technologies, and develop approaches that have the potential to transform patient care. This is a full-time training position of up to three years in duration. The start date for the 2026 Novartis BR Postdoctoral Fellowship Program cohort is October 1, 2026. Please confirm your availability to meet this date in your cover letter. Cardiovascular disease remains a major unmet medical need and a strategic focus for Novartis. This fellowship offers a unique opportunity to develop and apply cutting-edge AI methods to integrate ECG, genomic, and proteomic data at an unprecedented scale. The Fellow will work with large, multimodal datasets from population biobanks, clinical studies, and real-world data to discover novel biomarkers, uncover disease mechanisms, and improve patient stratification. Through close collaboration with experts in data science, human genetics, and cardiovascular medicine, the Fellow will gain hands-on experience translating advanced analytics into insights that can inform the development of future therapies. At Novartis, our purpose is to reimagine medicine to improve and extend people’s lives. Through this program, you will grow as a scientist and future leader while contributing to discoveries that may ultimately benefit patients worldwide.

Requirements

PhD (or equivalent doctoral degree) in a relevant scientific discipline completed prior to the fellowship start date. The program is intended for scientists immediately following their PhD training (PhD conferred in 2026 only).
Demonstrated record of scientific achievement (publications, presentations, patents, or equivalent)
Strong commitment to learning, innovation, and professional development
Strong quantitative background in data science, statistics, machine learning, bioinformatics, computational biology, computer science, biomedical engineering, or a related field.
Programming proficiency in Python and/or R and experience working with large-scale datasets.
Hands-on experience applying statistical or machine learning methods to biomedical, clinical, imaging, signal, omics, or other complex high-dimensional data.
Experience developing and evaluating predictive models using modern data science approaches.
Ability to work independently while thriving in a collaborative, multidisciplinary research environment.
Strong communication skills, including the ability to explain complex analytical concepts to diverse scientific audiences.

Nice To Haves

Experience in statistical genetics or multi-omics analysis
Experience analyzing physiological signals such as ECG, with deep learning and foundation models for biomedical data

Responsibilities

Develop and apply multimodal AI/ML models and statistical methods to integrate ECG, genomic, and proteomic data for cardiovascular disease research.
Analyze large-scale multimodal datasets from biobanks, clinical studies, and real-world data sources to identify novel biomarkers and disease mechanisms.
Design, implement, and evaluate predictive and interpretable models linking physiological signals with molecular and genetic variation.
Collaborate with a multidisciplinary team of data scientists, geneticists, clinicians, and cardiovascular researchers.
Communicate findings through publications, conference presentations, and internal scientific seminars.