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planning. Who we are looking for The following requirements are mandatory: A doctoral degree in computer science, artificial intelligence, applied mathematics, physics, electrical engineering, or a closely
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eligibility is granted to applicants who hold a Master of Science in Engineering degree or a Master’s degree in Electrical Engineering, Engineering Physics, Energy Systems, or a closely related field. Selection
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submissions in clusters Further details The position is funded (Salary) via the SciLifeLab and Wallenberg National Program for Data-Driven Life Science (DDLS; https://www.scilifelab.se/data-driven/ ), a 12-year
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: https://www.umu.se/en/work-with-us/benefits/ . The candidate’s physical workplace will be in Umeå. Application You apply via our e-recruitment system Varbi. Log in and apply via the button at the bottom
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; rather, they will be developed in dialogue between teachers and researchers based on the needs and interests of the schools. For more information ULF at Chalmers see: https://www.chalmers.se/institutioner
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determined in accordance with the agreement “Avtal om tidsbegränsad anställning som postdoktor” (“Agreement on fixed-term employment as a post-doctoral fellow”). The selection process will start in August. How
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in practice when the dimension of the data-generating process exceeds the sample size, this project will contribute by developing the distributional properties of generalized inverses of the high
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framework for assessing physical and mental workload in high-performance training environments. The project investigates how wearable sensors, IoT technologies, environmental sensing, mobile applications
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will strengthen your application: It is highly meritorious if the doctoral degree has been obtained within the last three years prior to the application deadline. Experience with differentiable physics
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candidate should be able to show research experience in national and/or international projects on process modelling and simulations, including both steady state and dynamic modelling and techno-economic