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promoters. You will train and evaluate predictive models in model/crop species with different levels of genome complexity. You will work very closely together with your dry-lab colleagues for data processing
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single-cell profiling and predictive artificial intelligence models, you will engineer synthetic promoters controlling context-specific gene expression in Arabidopsis. You will develop high-throughput
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on “Maternal Immune Activation” involving the development of novel artificial intelligence methods (graph and geometric deep learning, LLMs, …) working on methods for predictive multi-omics integration
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proteins with regard to their role in the development of cancer, the emergence of therapy resistance, as predictive markers to guide therapy and as therapeutic targets. Your Responsibilities Independent
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domains. The scientific outcomes are expected to be significant in: Earth system science – by improving models of Earth surface evolution and enabling better predictions of landscape response to climate
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to integrate various structural biology data (NMR, SAXS, FRET, EPR) as well as computational models and simulations to create and interpret conformational ensembles of disordered protein regions, with the goal
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computational models to map co-expression networks and predict systemic disease transitions. Characterise intestinal microbiome changes and their correlation with inflammatory diseases. Computational modelling
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duties or disabilities – just tell us what you need. Candidate profile The successful candidate will join four scientists on the FIS2-BADGE project, iteratively collaborating to develop predictive
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learning (AI/ML) being a major focus. Many of the laboratory's interests center around the identification of small molecules using mass spectrometry data, and the use of language models to predict
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, predict, and treat diseases. You will work with multimodal biomedical datasets including omics, imaging, and patient data and apply cutting-edge AI models such as graph neural networks, transformer