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Field
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field (e.g., Environmental or Soil Sciences, AI, Data Science, Geosciences). Basic knowledge of soil science and strong interest in AI and soil health. Experience with data analysis/modelling and
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will be co-supervised by LATMOS (A. Määttänen) and LMD (A. Podglajen), and will work within a team of researchers specialising in atmospheric physics and research engineers with expertise in modelling
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will contribute to the institute’s research focus on information retrieval (IR). Responsibilities: · Developing neural retrieval and ranking models to support the effective retrieval of scientific
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build upon the existing Alpha framework (originally developed to model protein quality, see https://alpha-tool.eu ) and further develop it as a research-oriented modelling and demonstration platform
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through microstructural characterization and comparing experimental observations with thermodynamic model predictions. Publishing your findings in peer-reviewed international journals and presenting
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necessary for this position. Where to apply Website https://www.academictransfer.com/en/jobs/359724/phd-urban-sound-modelling-for-a… Requirements Additional Information Website for additional job details
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on a microeconomic theoretical foundation, applied econometrics and economic modelling to analyse both uptake and consequences of uptake. Furthermore, the project will use a variety of data including
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for part-time employment. Starting date: 27.03.2026 Job description:PhD position on physics-based machine learning modeling for materials and process design Reference code: 2026/WD 1 Commencement date
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this project, we will be in the ideal position to uncover mechanism of action of these disease relevant bacteria. This work will include cell culture work (cell lines and primary cells) as well as mouse models
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microscopy) Flow cytometry Mammalian cell culture (cell models, including iPSC-derived cell lines) Lysosomal immunoprecipitation for multi-omics analysis Chemical synthesis Integration of biochemical, cellular