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computational and machine learning approaches, you will decipher genomic regulatory programs and infer the evolutionary patterns of gene regulatory networks in cortical neurons, study their developmental origin
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You will be hired as one of two team-leads of a consortium of several PhD students and master students working on the development of Covalent Organic Frameworks, photocatalytic semi-conductors for a
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-leads of a consortium of several PhD students and master students working on the development of Covalent Organic Frameworks, photocatalytic semi-conductors for a range of selective oxidation reactions
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of CLiPS, which focuses on the application of statistical and machine learning methods, trained on corpus data, to explain human language acquisition and processing data, and to develop automatic text
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spectroscopy. It is expected that the researcher will co-supervise a starting PhD student on the same project. The project will require a diverse set of interdisciplinary skills in live 3D microscopy, organoids
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to the general public Research proposal writing, funding acquisition Other administrative duties, for example related to the organization of a workshop, etc. Your profile PhD in Mathematics, Theoretical
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engineeringEducation LevelPhD or equivalent Skills/Qualifications •PhD in physics, materials science, nanotechnology, or a related discipline •Strong background in low-temperature physics, transport measurements, 2D
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biopharmaceuticals. The research at CMB pushes the boundaries of biomolecular and bioinformatics research and engineering technologies. VIB.AI studies fundamental problems in biology by combining machine learning with
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scale: PD1 to PD4 (doctoral degree) → Required diploma: PhD ABOUT GHENT UNIVERSITY Ghent University is a world of its own. Employing more than 8,000 people, it is actively involved in education and
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also opens new avenues for the design of climate-resilient crops. You will apply AI strategies to learn the regulatory syntax encoded by the Arabidopsis genome using single-cell transcript data as