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the design and analysis of learning-enabled systems. At STAR, we tackle key questions like: - How can we build systems that are both intelligent and inherently trustworthy? - What methods ensure reliability
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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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). This position offers a unique opportunity to collaborate closely with researchers across the Division of Marine Technology at Chalmers University, with a focus on maritime transportation risk analysis. Project
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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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engineering, antenna systems, signal processing, image analysis, automatic control, automation, mechatronics, and communication systems. With a dynamic and international environment of around 250 employees from
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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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, medical analysis and treatment, material processing, etc. You will be given the opportunity to work in one of the world-leading groups in the area and combine device characterization with simulations
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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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Python and MATLAB Documented experience with analysis of complex scientific data e.g. through machine learning What you will do execute experimental tasks, such as planning of experiments alone or together
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thermal analysis (DSC, TGA). Additional experience in rheology, vibrational spectroscopy (IR, Raman), and X-ray diffraction (WAXS, SAXS) is a plus. Excellent collaboration skills, due