140 machine-learning "https:" "https:" "https:" "https:" "https:" "UCL" "UCL" positions at Chalmers University of Technology
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an innovative spirit, in close collaboration with wider society. Chalmers was founded in 1829 and has the same motto today as it did then: Avancez – forward. Where to apply Website https://academicpositions.com
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technology, and to build a Swedish quantum computer). Within AQP, the group of Anton Frisk Kockum has the overarching goal of providing humanity the tools to understand and use large quantum systems. Working
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at the division of Computer and Network Systems , where we design secure, dependable and high-performance computer and communication systems that meet the demands of an increasingly digital and interconnected world
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. The position bridges machine learning and molecular science, with opportunities for collaboration, mentorship, and impactful research. About us The Department of Computer Science and Engineering (CSE
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initiative started in 2018 with the purpose of advancing Swedish academia and industry to the forefront of quantum technology, and to build a Swedish quantum computer). For the research on quantum optics with
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–classical algorithms or optimization methods Background in uncertainty quantification, reduced-order modeling, or machine learning Experience collaborating in interdisciplinary research teams A doctoral
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methods relying on machine learning, artificial intelligence, or other computational techniques. The applicant is expected to develop and apply data-driven and machine learning-based methods. Special
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, machine learning, etc. Building a quantum computer requires a multi-disciplinary effort involving experimental and theoretical physicists, electrical and microwave engineers, computer scientists, software
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your application: Experience in system identification and machine learning is a merit. What you will do Perform research, developing your own scientific concepts and communicating the results of your
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complex behavior under demanding operating conditions presents a significant modeling challenge. This project addresses that challenge by combining machine learning with constitutive modeling, while