15 phd-data-maining Postdoctoral positions at Chalmers University of Technology in Sweden
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The applicant should have a PhD degree in transport, operations research, applied mathematics, computer science, or similar topics. Experience with optimization, data-driven or machine-learning skills
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knowledge base. Main responsibilities include: Conduct benchmarking and further development of risk assessment models and components. Investigate the reliability of accident data, including cross-validation
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-related activities at the department (20%), including supervision of MSc and PhD students Possibilities for international collaboration and research exchanges Access to pedagogical training and development
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fellow, your main responsibilities are to lead and conduct research, and guide junior PhD students. You will have the freedom to contribute with your ideas, and collaborate with other senior researchers
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datasets. By integrating data-driven insights with innovative modeling, this interdisciplinary project aims to enhance our understanding of vulnerability, resilience, and adaptation, ultimately informing
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project Participate in teaching activities Qualifications You have a PhD degree in a field related to High Performance Computing or Computer Architecture. You are highly motivated, self-propelled, energetic
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are focused drivers and enablers in our work. Our goal is to perform high quality research and education at bachelor, master and doctoral levels. The postdoc is expected to conduct high-quality research in a
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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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15 Mar 2025 Job Information Organisation/Company Chalmers University of Technology Research Field Computer science » Other Researcher Profile Recognised Researcher (R2) Country Sweden Application
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Shipping, Naval Architecture, Ocean Engineering, Automotive, and Mechanical Engineering. M2 fosters strong collaboration between academia, industry, and society for effective knowledge transfer. With