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are seeking a motivated and enthusiastic colleague with strong computational skills in the analyses of complex data sets to join our teams. About the project We have generated advanced brain on chip models
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electrophysiology to translational models, including animal studies and analyses of human tissue samples. This full-stack methodology enables us to directly link molecular channel function with disease phenotypes
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critical roles of ion channels—particularly the TRP superfamily—in physiological and pathological processes. Our interdisciplinary approach spans from foundational electrophysiology to translational models
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member's task is strongly intertwined with the tasks of the other team members. You will design, train and apply generative models that learn how to complete missing wedges in the reciprocal space of crystal
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models recapitulating aspects of neural-microglia interactions in neurodegenerative diseases at Ghent University. Lipid accumulation in microglia is a hallmark of neurodegenerative diseases such as
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diverse academic backgrounds to contribute to our projects in areas such as: Network Security, Information Assurance, Model-driven Security, Cloud Computing, Cryptography, Satellite Systems, Vehicular
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-scale screens to study fundamental principles in molecular and complex trait genetics using microbes as model systems. Our core technology MAGESTIC (https://doi.org/10.1038/nbt.4137 ), a CRISPR/Cas9-based
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datasets. Your focus will be on implementing and training generative models to decompose cylindrical projections. You will solve and refine the structures from the resulting decomposed data. You will map
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application form and be sure to include the following attachments: a 1-pagemotivation letter, a short (1-page) project proposal/idea on our or other bird model species, and your CV. The selection committee will
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-scale screens to study fundamental principles in molecular and complex trait genetics using microbes as model systems. Our core technology MAGESTIC (https://doi.org/10.1038/nbt.4137 ), a CRISPR/Cas9-based