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to quality and form an integral part of KTH’s core values as a university and public authority. Learn more about our benefits and what it's like to work and grow at KTH. Trade union representatives Contact
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for latent variables and their connections to modern machine learning. The project combines methodological research in statistics with applications to large-scale social science data. The successful candidates
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subject area. The department has education assignments in engineering programs and master's programs. More information is available on our website . (https://kemi.uu.se/angstrom/?languageId=1 ). Work duties The
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, or a related field. It is an advantage if you have experience with probability theory, reinforcement learning, or Markov Decision Processes. Coursework or project experience in multi-agent systems
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initiative. You are curious, eager to learn, and have a genuine interest in technology. What we offer When you join our MAX IV team, you step into a world of front edge science. We make the invisible visible
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equivalent foreign degree, obtained within the last three years prior to the application deadline Experience with simulation frameworks, system-level performance evaluation, or machine learning, is highly
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at: www.ftf.lth.se , www.nano.lu.se , https://kaw.wallenberg.org/en/research/semiconductor-bandgap-key-future-green-electronics, https://c3nit.se/ Subject description The purpose of this project is to develop a data
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Learning has an open position for a doctoral student with a background and strong interest in deep generative learning and computer vision/remote sensing. The successful candidate will join a project funded
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, calorimetry, and synchrotron experimental measurement techniques. Knowledge of AI-based and machine-learning methods is also beneficial. For further information about a specific subject see General syllabus
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. The postdoctoral researcher(s) will join an international research environment at Umeå University, including Stat4Reg (www.stat4reg.se ), which develops statistical and machine-learning methods for register data