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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
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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
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collaboration with the group led by Asst. Prof. Gina Panopoulou. This includes code development, analysis and interpretation of data, writing manuscripts for publication and presenting work at relevant
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processing Programming skills (e.g. Python or MATLAB) Statistical analysis A strong publication record * The date shown in your doctoral degree certificate is the date we use, as this is the date you have met
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. Project overview The project involves applying advanced statistical analysis, machine learning techniques, and modeling approaches such as agent-based modeling to analyze diverse climate and socioeconomic
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work will involve both chemical analysis and mathematical modeling. In addition, you will be responsible for writing research papers and sharing your findings. You will also collaborate with partners
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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
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on their potential use in zinc-ion batteries. The role offers the opportunity to collaborate with both national and international partners, with possibilities to travel and visit external labs. Project
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Mega Watt Charging Stations (MCS) are an essential prerequisite for de-carbonasing long-haul trucks in Europe. As a postdoc you will be part of the EU project MACBETH where excellent research about