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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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the fundamental aspects of transcriptional control, this project also opens new avenues for the design of climate-resilient crops. Supported by single-cell profiling and predictive artificial intelligence models
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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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temperature signalling in plants, such as the model plant Arabidopsis thaliana and the crop plants wheat and soybean. To unravel this, we focus on dynamic changes in protein phosphorylation status, since
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information see https://www.kuleuven.be/personeel/jobsite/jobs/60473129 Job description Design and implement chemometric and machine learning models (e.g., PCA, PLS-DA, clustering, CNNs) to classify
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subteam, working closely with an experienced lab technician, two dedicated PhD students, and two postdoctoral researchers on a project focused on in vitro screening methods, including the use of organoids
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working on a third-party funded project funded by the Luxembourg National Research Fund (FNR) on 'Rule of Law Principles - which model for the Global South?' The project focusses on the transition
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in January 2025. The overarching goal is to identify the key factors controlling microbe-mediated carbon storage in the ocean, with a focus on using model microbial systems in the lab. We specialize in
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in January 2025. The overarching goal is to identify the key factors controlling microbe-mediated carbon storage in the ocean, with a focus on using model microbial systems in the lab. We specialize in