312 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "UCL" Fellowship positions in Norway
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- University of Oslo
- UiT The Arctic University of Norway
- University of Stavanger
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- University of Agder
- NTNU Norwegian University of Science and Technology
- OsloMet – Oslo Metropolitan University
- University of Inland Norway
- CICERO Center for International Climate Research
- CMI - Chr. Michelsen Institute
- Høgskulen i Volda
- Integreat -Norwegian Centre for Knowledge-driven Machine Learning
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- Simula Research Laboratory
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. Qualification requirements A PhD degree within neuroscience, psychology, medicine, machine learning or biology or equivalent. Doctoral dissertation must be submitted for evaluation by the closing date
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design and in silico validation intimately connected to experimental validation. In this project, you will develop machine learning methods and apply them in an interdisciplinary environment spanning
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combine intracranial electrophysiological recordings in humans with behavioral experiments and advanced analytical approaches, including machine learning and statistical modeling. It has two main objectives
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at the University of Oslo. Postdoctoral fellows who are appointed for a period of four years are expected to acquire basic pedagogical competency in the course of their fellowship period within the duty component of
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participation in the war in Myanmar since the 2021 military coup d’état. This responsibility includes a mapping of the conflict’s digital war ecology and focusing in on a specific example of remote participation
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), enabling cross-contextual learning and refinement of policy recommendations. A postdoc with expertise in urban built environment studies and qualitative social sciences will play an important role in
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public defence are eligible for appointment Strong programming and artificial intelligence/machine learning skills The candidate’s research proposal must be closely connected to the call and the research
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participate in democracy, our centre tackles the promise and peril of hybrid intelligence—human and machine working and learning together. AI LEARN’s mission is to establish an internationally leading
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solvers, with the goal of exploiting models of various complexity, ranging from high-performance computing, via reduced-order models to data-driven (machine-learned) representations. In particular, we
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to: compositional multiphase reservoir simulation upscaling or screening methodologies optimization of well positions and control strategies economic assessments machine learning or proxy-model based methods field