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project on geographic patterns of crimes and their environmental determinants, developing and employing (new) GeoAI and quantitative research methods . More specifically, the candidate will focus on 1
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outcomes, including several spin-off companies. Reactor and process developments go from millilitre scale to full industrial scale YOUR JOB The development of microbial management plans for contaminants
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learning algorithms. The two PhD students hired through this vacancy will primarily contribute to the development of debiased learning methods and assumption-lean modeling tools, and their application
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: 'digital twin' forests. Within your work, you will: (1) use 3D terrestrial laser scanning data to further develop the creation and implementation of digital twins of forests for optical and microwave
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to resolve spatial inhomogeneity and dynamics in the structures. Next to this, you will develop electrical pump –optical probe methods to study the effect of charge injection/extraction on optical losses
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effective risk assessment and solutions. Trees4Adapt will 1) enhance empirical understanding of climate change, biodiversity loss, their interdependencies, and how these influence risks; 2) develop evidence
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(ESEE) conference at the Faculty in June 2026. The specific topic for the doctoral dissertation will be co-developed during the first 6 months of the position within the research lines of the supervisors
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: the aluminum oxide layer is fully recyclable. As a PhD student, you will contribute to the development of multiphysics (electromagnetic & thermal) models for windings of electric motors. Also, you will study how
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to the development of neuroadaptive learning technologies grounded in real-time human feedback. Job profile We are looking for a highly motivated PhD candidate with a recent Master's degree in engineering and a strong
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, storage and demand. YOUR TASKS You will develop mathematical models and metaheuristic algorithms for complex optimization problems in the context described above, see e.g., https://arxiv.org/abs/2503.01325