25 collaborative-learning Postdoctoral positions at Technical University of Denmark in Denmark
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-learn, PyTorch) or physics-informed neural networks for thermal systems is a plus. Excellent communication and collaboration skills across disciplines. We offer DTU is a leading technical university
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computer architecture. Responsibilities and qualifications You are expected to conduct independent research in collaboration with and under the guidance of experienced colleagues. Additionally, you will be
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Job Description Are you a talented, self-motivated, and collaborative researcher who thrives in multidisciplinary environments? Are you excited by the idea of studying the carbon cycle in
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activities Collaborate with researchers across Denmark and Europe in an interdisciplinary environment Help coordinate project efforts across En’Zync partners, including DTU, Aarhus University, the Danish
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for innovative new projects (both local and international collaborations); Establish and maintain collaborations with partners within and outside DTU, as well as with private and public sector partners; Teach and
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(e.g., based on physiological signals or direct inputs from occupants) and developing algorithms, including machine learning methods. The work will include statistical modelling, data-driven modelling
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-mortem and fractographic analysis Collaborating with colleagues at DTU Energy and industrial partners to improve the reliability of SOEC stacks Publishing your research results in relevant peer-reviewed
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Postdoc in development and testing of electrodes for liquid alkaline water electrolysis - DTU Energy
to lifelong learning students. We have about 300 dedicated employees. Read more about us at www.energy.dtu.dk . Technology for people DTU develops technology for people. With our international elite research
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multidisciplinary team will work in close collaboration with the Francavilla-led team at the University of Manchester, UK, and with several scientists with diverse background in Denmark and abroad. Dr Schoof’s team
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the disparities. While foundation models offer great promise for creating more robust machine learning models for a wide array of tasks, it remains an open problem how to foresee their biases across that wide array