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– Adsorption and reaction of small gaseous non-metal oxides on model mineral particles under light illumination. The position is for four years of doctoral studies, including participation in research and
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algorithms for Bayesian machine learning with applications in e.g., medical image analysis. The doctoral student position is offered within the machine learning project “The Challenges for Machine Learning in
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factor in the design of the project. The project and research environment will give the dedicated student the opportunity to train as a first-class researcher. A person who is employed as a PhD student
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of Clinical Microbiology at Umeå University. The position is open to computational scientists with an interest in single-cell metagenomic analysis. Applications will be accepted until December 11th, 2025. About
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Statistics, and Analysis & Modelling. The Discrete Mathematics group consists of 10 faculty members whose research covers a broad range of modern combinatorics. The group runs a weekly seminar and welcomes
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simulations, computational modelling, and/or data-driven analysis to assess the impact of different climate policy pathways. Developing interactive AI-based narratives that communicate climate risks in ways
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mapping, and scenario analysis. The project is led by Professor Frank Dignum (main supervisor) and Dr. Jason Tucker. Requirements The general admission requirements for doctoral studies are a second- cycle
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. The candidate will be engaged in synthesis of new materials and characterization of these materials using BET/DFT surface area analysis, DSC, x-ray diffraction, Raman and IR spectroscopy etc. It is also planned
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grounding, in other words, the linking of elements of natural language (words, phrases, or sentences) to visual inputs (such as images or video) in a meaningful way. The position is part of an on-going
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experience in standard biochemical analysis methods, advanced molecular cloning techniques, CRISPR/Cas9 and experience in culturing and transfection of eukaryotic parasites. Experience in light microscopy