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execution. This work involves creating frameworks for adaptive decision-making, using techniques from operations research and machine learning. This particular thematic area will be supervised by Associate
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the next generation of PV technologies for beyond 2030. The new postdoctoral research position will use materials modelling techniques (DFT, molecular dynamics, machine learning potentials) to investigate
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difficulties in the research projects. It is essential that you hold a PhD/DPhil in a quantitative discipline (eg Operations Research, Management Science, Statistics, Machine Learning, Applied Mathematics
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talented and self-motivated scientist with a strong expertise in cardiac molecular imaging with a demonstrably flexible and adaptable mindset and a track record of learning and developing new analytical
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engineering, or related disciplines who are passionate about applying machine learning to real-world clinical challenges. The successful candidate will lead the development and validation of predictive models
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Programming skills Good at interacting with people, communicating, and working as part of an international team Self-organised, able to conduct independent learning Presentation skills (evidence of
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Samuel Kaski’s research group Probabilistic Machine Learning is searching for postdocs to work on AI fundamentals in exciting projects. The work includes collaboration with ELLIS Institute Finland
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criteria PhD qualified in Neuroscience, data analytics, computer science or a related discipline Strong publication record commensurate with career stage, demonstrating independence and impact. Proven
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50 Faculty of Life Sciences Startdate: 01.11.2025 | Working hours: 40 | Collective bargaining agreement: §48 VwGr. B1 lit. b (postdoc) Limited until: 30.06.2029 Reference no.: 4637 Explore and teach
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fingerprint identification (RFFI) for Wi-Fi. You will design novel RFFI algorithms and further evaluate their performance using practical testbeds such as software-defined radio platforms. You should have a PhD