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and vegetation development. This PhD project is part of a larger interdisciplinary research initiative aiming to enable a transition towards more data-driven and environmentally responsible maintenance
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. The EISLAB division at Luleå University of Technology conducts research in electronic systems design, sensor systems, cyber-physical systems, the Internet of Things and machine learning, and works on
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statistical model calibration, machine learning and data analysis. The research environment is international and interdisciplinary, with close links between fundamental method development and technically
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nutrition, such as: analysis of time series data and dynamic processes, where signals and responses evolve over time. statistical modelling, AI, and machine learning on large epidemiological cohorts, diet and
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.) You will work with one of the most comprehensive multimodal datasets available, enabling research at the frontier of data-driven biology. What you’ll do Develop and train large-scale machine learning
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one or more of the following areas is meriting: Bayesian statistics, mathematical modelling, probabilistic machine learning, deep learning, large language models. Rules governing PhD students are set
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data with computational modeling Programming skills in Python, R, or another relevant language. Interest in machine learning, statistical modeling, structural bioinformatics, or analysis of large-scale
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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
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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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Documented ability to work in Python Experience with machine-learning methods for record linkage and text analysis Documented experience with machine-learning methods for image-to-text transcription