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to the development of state-of-the-art nuclear-reaction models and evaluated nuclear-data libraries, supporting safe, reliable, and competitive technologies for both existing and future nuclear-energy systems, as
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high-resolution mass spectrometry, in vitro pharmacological characterisation of new psychoactive substances, as well as metabolomics and machine learning. As a PhD student, you devote most of your time
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software or similar languages and experience with modern machine learning and deep learning frameworks parallel computing using clusters like UPPMAX and GPUs for high-performance computing and parallel
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perform both empirical and theoretical work. You will learn how to collect and analyse data within your research area as well as communicate your results at national and international conferences and in
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research experience in e-health, digital health or a related field experience of, or a documented interest in, machine learning, AI methods or large language models (LLMs) in clinical or health-related
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will be broad, but primarily include: Analyzing large data sets for input into assessments and biological advice Follow the development and support the transition to next-generation assessment models
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Development Design new statistical and machine learning models tailored to this emerging omics modality. Multimodal Data Analysis Work with high-dimensional datasets combining quantitative RNA features
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communication. The PhD student will have opportunities to develop skills in experimental design, laboratory and field research on animals, statistical modelling and programming, evidence synthesis, climate
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revisited through mutual feedback and open communication. The PhD student will have opportunities to develop skills in experimental design, laboratory and field research on animals, statistical modelling and
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or heterogeneous environmental datasets Familiarity with spatial analysis, GIS, or geospatial data workflows. Experience with machine learning, modelling, or systems analysis approaches Interest in resilience