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Field
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researchers recruited into the WISE program will be a part of the WISE Research School https://wise-materials.org/research-school/ an ambitious nationwide program of seminars, courses, research visits, and
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the supervision of master’s and/or PhD students. Astrophysics research at KTH is part of the Oskar Klein Centre for Cosmoparticle Physics (OKC) at the AlbaNova University Centre (https://www.okc.albanova.se ). OKC
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to teach on the undergraduate/master’s level. The position is meritorious for future roles in academia, industry, or the public sector. Contract terms Full-time temporary employment for a maximum of two (2
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machine-learning tools. Data analyzed include precursors such as volatile organic compounds, aerosol number and mass concentrations, chemistry, biological particles, cloud and ice condensation nuclei, light
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on animal behaviour and welfare in pigs, where behavioural science is integrated with artificial intelligence and deep learning for the assessment of animal welfare. You will have a central scientific role in
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statistics and machine-learning–assisted approaches, in close interaction with data science collaborators Active collaboration across disciplines spanning spectroscopy, soft matter and nanomaterials
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and biodiversity for the future use and conservation of forests. More information: https://www.slu.se/en/wiforce . The work aims to generate new knowledge and contribute to the continued development
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benefits. Equality, diversity and equal opportunities are essential to quality and form an integral part of KTH’s core values as a university and public authority. Learn more about our benefits and what it's
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network modelling and machine learning for regulatory inference. - Functional validation of candidate TE‑CREs in spruce using UPSC transformation and somatic embryogenesis pipelines; evaluating drought
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metaproteomics approaches Analyzing large-scale multi-omics and clinical datasets to investigate individual metabolic responses to diet. The work includes applying advanced statistical and machine learning methods