117 data-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"U.S" positions at Technical University of Denmark
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grant from the European Research Council. The primary challenge in powering AI chips and future data center systems is enhancing the efficiency of voltage regulator modules (VRMs) to meet their
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for candidates with demonstrated experience in both qualitative and quantitative data management and analysis, particularly related to interviews, surveys, text analysis, and collaborative processes. Additional
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of computer-aided tools for chemical and biochemical product and process modeling, process synthesis, design, analysis and operation. The tools are applied in the chemical, petrochemical, pharmaceutical
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employment is 21 months. Expected starting date is 1 June 2026, or according to mutual agreement. You can read more about career paths at DTU here . Further information Further information may be obtained from
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join us. At DTU Chemical and Biochemical Engineering, you will operate at the forefront of computational modelling, integrating high‑fidelity CFD with advanced compartment models and modern data‑driven
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data. You will encode prior knowledge of the collisional processes in tokamak fusion plasmas using sophisticated numeric simulation codes, which will enable you to analyze data from tokamak experiments
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metabolic data and correlate NMR readouts with physiological function. Preferred Qualifications: PhD in Bioengineering, Chemistry, Biophysics, or a related field. Extensive hands-on experience with organ-on-a
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attention to confidentiality Understanding of GDPR and research data governance in collaborative projects An entrepreneurial and solution-oriented mindset, with interest in translational research Experience
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Job Description Are you passionate about environmental contaminants, food safety, marine ecosystems, and creating real-world impact through cutting-edge analytical and data-driven approaches? Do you
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assessment. Design and train reinforcement learning agents to optimize operational safety. Build and validate dynamic Bayesian network models integrating empirical and synthetic data. Conduct scenario-based