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community. We will monitor the evolution of root trait development using both destructive and non-destructive methods, and assess how the activity of microfauna controls this development, both directly and
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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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factors that accelerate material deterioration (e.g., hydrogen embrittlement, corrosion). The primary tasks include developing advanced multiphysics frameworks (finite element/phase-field methods
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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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techniques) at UGent combined with machine learning, deep learning and data fusion modelling to enable development of novel decision support systems for variable rate fertilization and manure application. He
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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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, we understand little about the genomic underpinnings of evolution and adaptation in diatoms. Within DIADAPT, we will investigate the genomic processes that underlie adaptation to climate shifts in
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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