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for developing reliable predictive models and ensuring safe and robust component design. This position is part of the CastAl project, which aims to identify the mechanisms governing stochastic fracture in HPDC
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related to models and multiple sources of data describing ecological dynamics. The PhD project will address the following aims: 1) Develop efficient tools for learning about models from data, 2
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leading universities, internationally recognized for high quality in research and education. As a societal institution, we shall contribute to sustainable and democratic development and be an attractive and
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and fair synthetic data with good utility. We are particularly interested in applicants motivated to develop machine learning methods that matter for society — including fairness, privacy, ethical
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sheet evolution, methane hydrate fluxes, or applying machine learning to geosciences to reconstruct glacial histories and project future ice sheet behavior. Please read this interview for more details
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and magnetic data to map subsurface structures 3. Basin modelling, with knowledge of sedimentary processes and tectonic evolution The project would contribute to mapping the thickness of sedimentary
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-development Where to apply Website https://www.jobbnorge.no/en/available-jobs/job/294721/phd-research-fellow-in-fi… Requirements Research FieldBiological sciencesEducation LevelMaster Degree or equivalent
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stellar tidal streams) around disk galaxies. The successful candidate will design, perform and analyse idealized and cosmological hydrodynamical galaxy formation simulations, develop or improve models
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science). The candidate is expected to contribute toward developing wholistic adaptive management systems. BioM is an interdisciplinary Convergence Environment under UiO:Life Science , involving
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experience and expertise in web-scale data curation, development of large language models (LLMs), and in-depth LLM evaluation. LTG has a strong commitment to open-source resource and software development