Data Science & AI Innovation Postdoctoral Fellow d42

Das ist der Job

The goal: help re‑define how clinical trials are analyzed, simulated and designed.

Darum lohnt es sich

The successful candidate will collaborate across multidisciplinary research teams and contribute to scientific publications and innovation in AI‑driven drug development. /p pLocation: Basel, Switzerlandbr/Duration: 3 yearsbr/Program start date: October 1, 2026 /p h3About the Role /h3 pWe 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. /p pAs a Postdoctoral Research Fellow, you will join data42 in Basel, and pursue an innovative research project at the forefront of biomedical science and drug discovery.

You will work with real clinical trial data at scale, combine methodological ML research + translational impact, collaborate with experts across ML, biology, and medicine to build the foundations for digital twins and in‑silico trials. /p pbWhy Join the Program? /b /p pThe Novartis Biomedical Research Postdoctoral Fellowship Program is designed to develop the next generation of scientific leaders, powering the future of medicine, through rigorous research, and immersive learning experiences, such as implementation of AI tools in biomedical research. /p pPostdoctoral Research Fellows benefit from: /p ul liGuidance from accomplished scientific leaders and subject matter experts /li liAccess to advanced technologies, platforms, and research capabilities /li liCollaboration across disciplines and organizational boundaries /li liA global and diverse community of postdoctoral fellows /li liDedicated programming designed to help fellows thrive throughout their careers. /li liPersonalized experiential learning opportunities through a Postdoc Practicum that empower fellows to explore new scientific domains, build cross‑functional expertise, and expand their impact beyond their primary research project. /li liOpportunities to present research, publish in leading journals, and build an international scientific network /li /ul pWe are entering a new era of biomedical research breakthroughs through the convergence of biology, technology, and artificial intelligence tools, and fellows are also supported in engaging with these emerging approaches. /p pThis is a full‑time training position of up to three years in duration. /p pbReimagining Medicine Together /b /p pAt 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. /p h3Key Responsibilities /h3 ul liDevelop and benchmark machine learning models for treatment effect estimation, patient stratification, and counterfactual outcome prediction from clinical trial data. /li liDesign and evaluate generative AI models for patient trajectory simulation, synthetic cohort generation, and virtual clinical trial applications. /li liDevelop methods that generalize treatment effect models across patient populations, disease cohorts, and clinical indications. /li liApply causal inference and explainable AI approaches to identify predictive and mechanistic biomarkers associated with treatment response and adverse events. /li liDefine and refine ML research strategy and experimental designs to achieve scientific research goals. /li liCollaborate with interdisciplinary teams spanning data science, translational medicine, oncology, immunology, and drug development to address high‑priority scientific questions. /li liDisseminate research through publications, conference attendance and internal seminars and presentations. /li /ul h3Essential Requirements /h3 ul liPhD (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 (graduated in 2026). /li liDemonstrated record of scientific achievement (publications, presentations, patents, or equivalent). /li liStrong commitment to learning, innovation, and professional development. /li liStrong foundation in ML (deep learning, probabilistic modeling, or similar) and statistics. /li liDemonstrated experience in deep model development including architecture and training task design. /li liInterest in Biology, clinical data and/or drug discovery. /li liInterdisciplinary communication skills. /li /ul h3Desirable Requirements /h3 ul liExperience in causal ML, representation learning, and generative models. /li liExperience of working in multidisciplinary teams. /li liExperience in developing machine learning models for regulatory applications. /li /ul pPlease note that we can only accept applicants who are eligible to work in Switzerland. /p /p #J-18808-Ljbffr ph3Summary /h3 pThe AI Innovation Postdoctoral Fellow will develop cutting‑edge machine learning and generative AI methods for treatment effect modeling, patient stratification, and virtual clinical trials using large‑scale clinical trial datasets.

The role combines methodological research in causal AI and predictive modeling with real‑world biomedical applications, aiming to improve clinical decision‑making, biomarker discovery, and trial design.

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. /p pOur fellows are empowered to ask bold scientific questions, apply cutting‑edge technologies, and develop approaches that have the potential to transform patient care. /p pbResearch Opportunity /b /p pThis is a unique opportunity to work at the frontier of machine learning and real‑world biomedical impact.

You will have the opportunity to build next‑generation models that go beyond prediction and learn treatment effects, tackle counterfactual reasoning at patient level, explore generative “digital patients” and synthetic trials.

You will have access to one of the richest biomedical data environments globally: clinical + biomarker + omics datasets from hundreds of thousands of patients across thousands of trials and real‑world data assets for validation and generalization.

Your project will contribute to improving patient stratification, biomarker identification, trial design and decision‑making with the aim of laying the foundations for future virtual clinical trials.