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Field
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spatial and temporal variability in sediment accumulation and vegetation development. This PhD project is part of a larger interdisciplinary research initiative aiming to enable a transition towards more
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support. The PhD candidate will develop and validate a hybrid methodology that combines established stochastic optimization with AI-based learning. The aim is not only to develop new algorithms, but also to
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of electrolyzer technologies, digital twins, model order reduction, system identification, power electronics, model predictive control, multi-objective optimization, machine learning, renewable-energy integration
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BMS constraints. Experience with system identification, uncertainty-aware modelling, large datasets, and machine learning. Evidence of research capability through a thesis, publications, conference
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this PhD project, you will build a self-driving laboratory platform that will help solving outstanding questions at the forefront of (photo)chemistry by implementing automated spectroscopy workflows
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. In addition, the following are requirements for the role: Strong programming and quantitative skills, particularly in Python and/or R. Experience in deep learning, machine learning, or large-scale
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: procedural or node-based production, AI or machine learning, or technical art. Applicants should have demonstrable programming or scripting experience and the ability to develop and evaluate working software
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AI and data science, particularly in dynamic settings where observations are collected sequentially and decisions influence future outcomes. This project will develop novel machine learning and
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31 Aug 2026 Job Information Organisation/Company Fondazione Bruno Kessler Research Field Other Researcher Profile Other Profession Positions PhD Positions Application Deadline 24 Sep 2026 - 23:59
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development work at the Norwegian University of Science and Technology (NTNU) for general criteria for the position. Preferred selection criteria Experience with machine learning and neural networks Basic