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is part of the ERC-funded project “Actively learning experimental de-signs in terrestrial climate science (ACTIVATE)”: https://www.mn.uio.no/geo/english/research/projects/activate/index.html The PhD
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collaborators. The position is part of the ERC Starting Grant “Actively learning experimental designs in terrestrial climate science (ACTIVATE)”: https://www.mn.uio.no/geo/english/research/projects/activate
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for appointment. Strong background in at least two of the following: quantum optics / quantum information / open quantum systems quantum control / optimal control machine learning (including deep learning and/or
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, and multimodal deep learning approaches that enable privacy-preserving learning across distributed healthcare data sources Support health outcomes with research in transformative applications in
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machine learning (including deep learning and/or reinforcement learning) numerical simulation of quantum dynamics Proven programming skills (e.g., Python/Julia/C++), including experience with scientific
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such as deep and statistical learning. Experience of executing research projects at the level of senior PhD student, including creating and debugging machine learning algorithem. Excellent communication
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for Science, and scientific discovery by advancing our understanding of interpretable machine learning models and their practical applications in real-world domains such as healthcare and science. Key
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30th September 2026 Languages English English Norsk Nynorsk English PhD Research Fellow in climate data analysis and machine learning emulation of hydrological models Apply for this job See
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Research Fellow – AI/Machine Learning in Pharmaceutical Data Job No.: 697007 Location: Clayton campus Employment Type: Full-time Duration: 11-month fixed-term appointment Remuneration: $86,195
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medicine. What can be funded? Research visits for postdoc Chinese scientists and researchers. Duration of the funding 3-12 months Value Monthly scholarship rate of 2.100 EUR Combined health-, accident and