University of Oslo - DSTrain Doctoral Programme - Machine learning on climate, health and biological data | Deadline: 06.04.2025
DSTrain
DSTrain is a 5-year postdoctoral programme that will award 36 postdoctoral fellowship positions of 36 months each in two calls over the programme period within the overarching frame of data science. The programme will train researchers and innovators with disciplinary, interdisciplinary and transferable skills and a foundation in data science methods enabling them to become Europe’s digital leaders across disciplines and sectors.
Machine learning is seeing increasing use within life sciences, due to the presence of complex relations between a typically large number of interacting entities. This happens both at micro scale, between and within cells, and at macro scale, where e.g. climate change is seen by WHO as the single biggest threat to human health due to the many ways that climate affects human and animal health. At both micro and macro scale, developing carefully crafted machine learning strategies is crucial to handle the typically limited amount of data, to learn robust models that generalise well across relevant contexts and to ensure explainability. This overall theme encompasses two more focused sub-themes, which are described in more following the link below.
DSTrain is a 5-year postdoctoral programme that will award 36 postdoctoral fellowship positions of 36 months each in two calls over the programme period within the overarching frame of data science. The programme will train researchers and innovators with disciplinary, interdisciplinary and transferable skills and a foundation in data science methods enabling them to become Europe’s digital leaders across disciplines and sectors.
Machine learning is seeing increasing use within life sciences, due to the presence of complex relations between a typically large number of interacting entities. This happens both at micro scale, between and within cells, and at macro scale, where e.g. climate change is seen by WHO as the single biggest threat to human health due to the many ways that climate affects human and animal health. At both micro and macro scale, developing carefully crafted machine learning strategies is crucial to handle the typically limited amount of data, to learn robust models that generalise well across relevant contexts and to ensure explainability. This overall theme encompasses two more focused sub-themes, which are described in more following the link below.
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Webseite | https://www.uio.no/dscience/english/dstrain/research-areas2025/informatics/machine-learning-on-climate-health-and-biological-/index.html |