Postdoc position – Explainable AI and Federated ML in Healthcare – Lugano, Switzerland

The position is funded by an EU (HORIZON) project featuring 13 project partners from 8 EU countries, including research institutions, medical centers, and companies. USI’s role will be the development of trustworthy, AI-based decision-support tools that will help health professionals and citizens reduce long-term risk of non-communicable chronic diseases (NCDs).

More specifically, the project envisions the usage of federated learning to ensure data privacy and the usage of XAI techniques including actionable counterfactual explanations in a privacy-aware manner using federated learning approaches.

• PhD (or near completion) in computer science or a related field with a focus on ML.
• Demonstrated quality of research performance, as evidenced by high-quality publications in top-tier ML / AI conferences and journals (e.g., ICML, NeurIPS, IJCAI, AAAI, Information Fusion, IMWUT, JMLR).
• Strong programming skills, including experience with Python and deep learning libraries (e.g., PyTorch, TensorFlow, and Sklearn).
• Strong command of English, both orally and in writing
• Previous research on XAI and/or privacy-aware ML (e.g., federated learning) a plus.