-
postdoctoral position in data analysis, where you will apply machine learning techniques to understand how resistance genes spread and to help detect infections caused by resistant bacteria. The position is part
-
to work on topics at the intersection of applied probability and analysis. The group around Pierre Nyquist currently consists of three PhD students and is focused on questions in probability theory and
-
and calibration of reports from various sources. Collect and analyse large-scale cross-industry accident data using FRAM (Functional Resonance Analysis Method) within LLMs to identify human-, technical
-
. The overall aim of this project is to address these challenges by: Developing new data-driven and physics-based models of battery behaviour. Designing advanced BMS algorithms for real-time monitoring and
-
applications, specifically targeting the prognosis and risk prediction of Heart Failure (HF) in patients. This research integrates AI safety, explainability, and multimodal medical data analysis to enhance