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prediction to process optimization. The focus of this PhD project is to develop and apply machine learning methods across three interconnected tasks: 3D microstructure characterisation. The student will
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, such as heterogeneity of data sources and communication constraints. By leveraging tools from statistical signal processing, machine learning, optimization, and mathematical modeling, the project aims
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are essential Additional qualifications Experience and courses in one or more subjects are valued: statistical machine learning, optimization, deep learning and signal processing. Rules governing PhD students
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. Optimal transport is a key mathematical concept that allows us to understand notions like inference and sampling as dynamic processes of probability distributions. Building on the theoretical insights, we
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of these subjects are valued: machine learning, automatic control, system identification, optimization, signal processing, filtering and smoothing, probabilistic modelling, dynamical systems
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technology Researcher Profile First Stage Researcher (R1) Application Deadline 14 May 2026 - 21:59 (UTC) Country Sweden Type of Contract Temporary Job Status Full-time Is the job funded through the EU Research
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the underlying principles. For us, it is equally important to study the impact of materials on biological processes as well as the impact of biological processes on materials. Our ambition is to foster a dynamic
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for the rapid conversion of biomass into hard carbons, significantly reducing the time and energy required compared to traditional processes. Additionally, the collaborative research aims to identify optimal