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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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sciences; Have experience in programming and/or quantitative data analysis; A keen interest in working across scientific disciplines, including physics, chemistry and biology; Excellent interpersonal and
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constraints such as sensitive devices running in a medical environment could be considered. Keywords for this project: code analysis, static analysis, reverse engineering, defense mechanisms, vulnerability
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, among other things, how the production of cement-based materials is affected by electrified processes that reduce carbon dioxide emissions, how trace elements such as rare earth metals are bound in
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the vicinity of perennial snow fields during the growing season. The PhD student will also be responsible for analysis of data from eddy covariance sensors, weather stations, and hydrological installations, as
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. The following achievements, skills and/or knowledge are considered particularly relevant: Experience in standard bioinformatic analysis related to genomic and genetics, e.g. experience with any
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the flexibility of neural methods. If successful, the work has the potential to advance applications such as automated theorem proving, knowledge-graph inference, and causal analysis. The Department of Computing
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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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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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of Mathematics and Mathematical Statistics, alongside Computational Mathematics, Mathematical Statistics, and Analysis & Modelling. The Discrete Mathematics group consists of 10 faculty members whose research