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goals. Our other research directions of interest include privacy of AI models trained on health data as well as training AI models on health data across national borders. Key responsibilities Design
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collaborators across the Nordics, Baltics and Europe present a fantastic opportunity to pursue your own ambitious research goals. Our other research directions of interest include privacy of AI models trained
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new characterization techniques. The research needs to be connected and communicated within the project and length-scale bridging modelling tasks are included. Part of the project collaboration happens
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nature experiences influence human time perception, and validate experimental results using real-world data obtained with citizen science. The project will also model the effects of nature on time
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perception, and validate experimental results using real-world data obtained with citizen science. The project will also model the effects of nature on time perception at the landscape scale under different
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and experimental models. Our research focuses on combining large-scale molecular data - including metabolomics, lipidomics, genomics, proteomics, glycomics, and microbiome data - with advanced
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during cancer, inflammatory diseases, and vaccination. The successful candidate performs laboratory experiments, including in vivo models, analyzes tissue and cell samples using imaging and omics
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methods of the research project are philosophical analysis and model-building. Responsibilities The main task of the appointed researcher is to conduct research into epistemological issues where the notion
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model is applied, with regular on-site presence required. The role involves some travel between LUT campuses and to international conferences. The position follows a system of 1,612 annual working hours
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‑classical algorithms to tackle domain‑specific, computationally demanding problems. Methods will be designed for near‑term (NISQ‑era) deployment, with models evaluated for expressivity, training stability