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Field
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University Duties This PhD project is part of the AFLOW consortium supported by the Swedish Energy Agency and focuses on multi-scale modelling of aqueous organic redox flow batteries, to build a predictive
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biogeography. A key focus lies in comparing past and present natural processes for predicting environmental changes. Start of studies in the winter semester (October) Opening of applications and publication
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currently lack reliable uncertainty estimates, limiting error detection and automation. The UMLFF project aims to develop next-generation MLFFs with built-in uncertainty predictions to enable safe, automated
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derived spheroids Develop predictive model of drug response by comparing 2D to 3D cellular systems Testing and validating the relevance of such models in patient tumour specimens Support and preparation
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of complex epidemiological and genetic data, in computational and population health sciences and in disease risk-modelling and risk-prediction. Eligibility criteria The project will suit students with strong
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modelling is a valuable tool to revealing the source of UTLS aerosols, the origin of water masses, and formation processes of cirrus particles. Your key responsibilities include: Preparation, operation, and
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SFI FAST: PhD position in Microstructure/texture evolution during extrusion of scrap-based Aluminium
physics- and data-driven models that deal with microstructure/texture evolution during extrusion to predict material properties of extruded profiles Collaborate with other researchers and industry partners
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ACHILLES, which brings together a broad spectrum of expertise, including basic research to identify novel biomarkers and therapeutic targets, mathematical and computational modeling to predict disease
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breeding programs and to support the reduction of methane emissions; a strong interest in statistical models, genomic prediction, and quantitative genetics, preferably with experience with one of more
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research in data-driven nutrition, health, and food science. With large-scale diet and health data, omics data, biomarkers, digital food and health services, we establish predictive models for evaluation