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microscopy data analysis, chemometrics, and machine learning. This position is ideal for a researcher who enjoys working at the interface of imaging, data science, and environmental monitoring. The project
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publication(s) in peer-reviewed international journals Experience in molecular biology, single cell approaches, imaging and electrophysiology are a plus Team player and great collaborator Strong interest in
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in close collaboration with academic and industrial partners, addressing topics such as cooperative automated driving, connected mobility systems, and intelligent transportation technologies. You will
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strong background in shape modelling, deep generative modelling (diffusion/transformers), or multimodal representation learning. You have strong programming skills, especially in Python, and preferably
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, for their analysis and optimization, we use tools such as artificial intelligence/machine learning, graph theory and graph-signal processing, and convex/non-convex optimization. Furthermore, our activities
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, including the selection and application of scale-up modelling approaches (e.g. process simulation, expert interviews, and the integration of scale and learning effects) for the identified scenarios
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multiple symbolic fields, including official political discourse, academic debates and selected cultural outputs. Methodologically, it adopts a mixed-methods approach, combining machine-learning–enhanced
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University is a logical choice for its staff and students. For this project, you will work as part of the “AlUla Inscriptions Corpus Analysis Project (AICAP).” This is a four-year collaboration between Ghent
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player and great collaborator Strong interest in interdisciplinary work at the interface between neurodegeneration, modeling, screening and machine learning Prior experience in iPSC modelling and CRISPR
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mechanism. The research is conducted in collaboration with other research teams across Europe (University College London, TU Delft, Max Planck Institute Germany, Flanders Institute of Biotechnology) See