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Field
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perturbation-based GRN inference for single-cell and spatial multi-omics data, to boost GRN quality and add the cell type and tissue heterogeneity dimensions to causal regulatory analysis. A deep learning
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) to ensure efficient roll-out of the system Profile MSc degree in Geophysics, Seismology, Earth Sciences, Physics, Engineering, or a related field Strong quantitative and analytical skills as
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of protein design with synthetic biology approaches - Previous work in interdisciplinary research environments Personal qualities such as analytical thinking, independence, collaboration skills, and scientific
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combines machine learning, legal applications, and empirical evaluation in collaboration with judicial partners. The project offers a unique opportunity to work on real-world, high-stakes AI systems in
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bespoke methods – including advanced data modelling approaches (e.g., machine learning, digital twin models) and AI techniques where appropriate – to provide novel solutions that enable sports to make
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mechanisms for distributed learning, real-time analytics, and AI-driven decision-making across heterogeneous environments (edge-cloud), contributing to the realization of intelligent and self-optimizing 6G
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skills (preferably Python) and experience with machine learning frameworks such as PyTorch or TensorFlow. Strong analytical and problem-solving skills and interest in mathematical research. Experience with
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dynamics. Particular emphasis is placed on opinion dynamics as well as distributed problems in coordination, optimization, and learning. The research encompasses both theoretical and computational aspects
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physiological MRI technique development within a translational research environment bridging analytical bioscience and neuro-oncology. Research Environment The successful applicant for a 4-year PhD studentship
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sensing systems Design and validate machine learning models for predictive monitoring of physiological states Analyse large experimental datasets and quantify sensor performance (accuracy, robustness