78 machine-learning-"https:" "https:" "https:" "https:" "https:" "https:" "Simons Foundation" PhD scholarships in Norway
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22nd February 2026 Languages English English English The Department of Electronic Systems has a vacancy for a PhD Candidate in Machine Learning for Energy Constrained IoT Systems Apply for this job
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25th February 2026 Languages English English English The Department of Materials Science and Engineering has a vacancy for a PhD Candidate in machine learning and large language models (LLMs
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interests through elective courses and secondments. • Blended Learning Approach: Our training combines intensive in-person workshops at partner institutions with regular interactive online seminars, journal
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promise and peril of hybrid intelligence—humans and machines working and learning together. Our mission is to establish an internationally leading interdisciplinary hub that advances foundational research
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broad range of areas, including causal inference and time-to-event analysis, clinical trials, epidemiology, high dimensional statistics, infectious disease, machine learning and mathematical modelling
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Norwegian master’s degree. Please see https://hkdir.no/en/ for more information about HK-dir’s general recognition. The review process by HK-dir may take some time. Therefore, please submit your application
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information and how to apply: https://www.jobbnorge.no/en/available-jobs/job/292175/phd-position-on-the-history-of-medical-plastics Where to apply Website https://www.jobbnorge.no/en/available-jobs/job/292175
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Duration: 3 years Start date: August 2026 at latest The students will be enrolled in the structured PhD programme in the Department of Bioengineering, Imperial College London. https://www.imperial.ac.uk
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grade background, you may be considered if you can document that you are particularly suitable for a PhD education. You must meet the requirements for admission to the faculty's Doctoral Programme: https
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selection criteria Experience with machine learning or other relevant AI technologies Experience with condition monitoring, preferably within maritime domains Knowledge of ship machinery and systems Good oral