Data Science & AI Innovation Postdoctoral Fellow d42

Das ist der Job

Define and refine ML research strategy and experimental designs to achieve scientific research goals.

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. Collaborate with interdisciplinary teams spanning data science, translational medicine, oncology, immunology, and drug development to address high‑priority scientific questions.

Experience working in multidisciplinary teams. Summary The 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.

Location: Basel, Switzerland Duration: 3 years Program start date: October 1, 2026 Application deadline: July 15, 2026, end of day About the Role As 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 alongside leading scientists in a highly collaborative, multidisciplinary environment while gaining exposure to the broader ecosystem that translates scientific discovery into medicines. Research Opportunity This unique opportunity enables you to help redefine how clinical trials are analyzed, simulated, and designed.

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

You will work with one of the richest biomedical data environments globally, combining clinical, biomarker, and omics datasets from hundreds of thousands of patients across thousands of trials and real‑world data assets for validation and generalization.

Key Responsibilities Develop and benchmark machine learning models for treatment effect estimation, patient stratification, and counterfactual outcome prediction from clinical trial data. Design and evaluate generative AI models for patient trajectory simulation, synthetic cohort generation, and virtual clinical trial applications.

Develop methods that generalize treatment effect models across patient populations, disease cohorts, and clinical indications. Apply causal inference and explainable AI approaches to identify predictive and mechanistic biomarkers associated with treatment response and adverse events.

Disseminate research through publications, conference attendance, and internal seminars and presentations. Essential Requirements PhD (or equivalent doctoral degree) in a relevant scientific discipline completed prior to the fellowship start date. Demonstrated record of scientific achievement (publications, presentations, patents, or equivalent).

Strong commitment to learning, innovation, and professional development. Strong foundation in ML (deep learning, probabilistic modeling, or similar) and statistics. Demonstrated experience in deep model development, including architecture and training task design. Interest in biology, clinical data, and/or drug discovery.

Interdisciplinary communication skills. Eligibility to work in Switzerland. Desirable Requirements Experience in causal ML, representation learning, and generative models. Experience developing machine learning models for regulatory applications. #J-18808-Ljbffr