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Field
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of response and develop predictive models. The work will involve analysis of large-scale datasets through multiomics integration, machine learning, statistical genetics, QTL analysis and development of genetic
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advanced AI and bioinformatics approaches to large-scale single-cell and multi-omics datasets. The project will focus on integrating these data to investigate cellular and tissue-specific signalling
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therapies emerge and precision medicine become increasingly achievable, understanding this heterogeneity is critical. This PhD will use large-scale routinely-collected healthcare data and cutting-edge
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and machine learning methodology to help deal with key challenges in developing such models in large-scale observational electronic healthcare record data. These models will be applied to important real
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magnetic depth profile in the layer stacks. We have extensive experience with both techniques (which will be performed at international large-scale international research facilities (such as ESRF in Grenoble
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Ballester (TAGC, Marseille). The student will benefit from the complementary expertise of the LBBE in evolutionary and comparative genomics and of the TAGC in regulatory genomics and large-scale functional
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on large-scale numerical simulations, instrumented experiments, and onboard robotic data could learn generic representations of local interactions between mobile robots and their environment, enabling
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advanced AI and bioinformatics approaches to large-scale single-cell and multi-omics datasets. The project will focus on integrating these data to investigate cellular and tissue-specific signalling
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PhD Position - Operando X-ray Characterization of Catalytic Interfaces for Chemical Hydrogen Storage
present the work at domestic & international scientific events. Work closely with colleagues across IHE, Forschungszentrum Jülich, partner universities, and large-scale light source facilities. Your Profile
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: Advancing machine unlearning, privacy-preserving techniques, and robust data curation. AI Safety: Ensuring robust alignment and safety in multi-agent LLM systems Efficiency: Streamlining large-scale model