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projects in data-driven nutrition, such as: statistical modelling, AI, and machine learning on large epidemiological cohorts, diet and health data analysis of omics data (metabolomics, proteomics, microbiome
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3 Apr 2026 Job Information Organisation/Company Sveriges Lantbruksuniversitet Research Field Other Researcher Profile First Stage Researcher (R1) Application Deadline 22 Apr 2026 - 12:00 (UTC
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numerical analysis, computational fluid dynamics, and uncertainty quantification with diverse applications. Our group maintains active collaborations with other divisions at Linköping University and broader
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programming capabilities with ample experience from previous projects are a requirement. Strong experience with Python, data analysis, and visualisation techniques is a bonus. The results of this work will be
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agricultural system and about registrar and/or survey data used for applied economic analysis of agriculture is of merit. Personal merits will play a significant role in the recruitment. About us The Swedish
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qualifications and relevance of previous studies Experience with quantitative methods, modelling, data analysis and/or geoscientific techniques, as well as fieldwork experience Scientific motivation and research
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, data analysis and/or geoscientific techniques, as well as fieldwork experience Scientific motivation and research potential Ability to work independently and collaboratively in an interdisciplinary
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data (HRMS) used for non-target analysis. The projects aims to develop a combination of supervised and unsupervise machine learning stragaties for pinpointing chemicals that have high toxicity
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analyses and document analysis. Depending on the sub-projects chosen, the research may also combine interviews with quantitative analysis. The doctoral student will become part of a leading research
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perturbation-based GRN inference for single-cell and spatial multi-omics data, to boost GRN quality and add the cell type and tissue heterogeneity dimensions to causal regulatory analysis. A deep learning