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Field
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impacts of our missions and respond to the major scientific challenges of our times as captured in the new ESA science strategy https://esamultimedia.esa.int/docs/EarthObservation
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patients from public repositories (including dbGaP). Develop and apply machine learning algorithms to associate patterns in the data with cancer progression and therapeutic response in prostate cancer
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the Norwegian educational system The purpose of the fellowship is research training leading to the successful completion of a PhD degree. For more information see: http://www.mn.uio.no/english/research/phd/ All
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for collaboration. About You The candidate must have obtained a PhD degree within 3 years and in the areas of digital healthcare, healthcare management, information systems, or other related areas; Excellent oral and
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or communications venues. Interest in mission-critical and critical infrastructure scenarios. What you will do Develop models, algorithms and optimization methods for resilient 6G transport networks, using machine
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end date may change, in which case the position ends accordingly. Job description This position is integrated into a high-impact Horizon Europe research project. As a PhD candidate, you will conduct
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development goals will be discussed and fostered throughout the postdoctoral period. The researcher will participate in tape-outs together with the PI and PhD students in the Adam’s group and benefit from our
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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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or communications venues. Interest in mission-critical and critical infrastructure scenarios. What you will do Develop models, algorithms and optimization methods for resilient 6G transport networks, using machine
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Date 09/24/2026 Priority Review Date (Note - Posting may close at any time) Job Summary We are seeking a highly talented PhD Student with experience and interest in state-of-the-art AI technologies, data