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microbiology. They learn contemporary microbiological, molecular genetic, bioinformatic, biochemical, analytical, and biophysical methods for studying microorganisms. Key research areas include physiological
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, cell culture, in vitro assay development, and data analytics. We are looking for candidates that are interested in these areas and who wish to deploy genetic engineering strategies to advance the safety
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related field. You bring a strong analytical background and are proficient in areas like geometric deep learning, signal processing, statistics, or learning theory. Knowledge of energy systems, multi-energy
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will apply machine learning — in particular physics-constrained symbolic regression — to discover compact analytical spin-Hamiltonians and their parameter dependencies. These Hamiltonians will feed large
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open to candidates with a strong interest in either: i) Radio/physical-layer intelligence (e.g., channel estimation, CSI prediction, edge-deployable deep learning), or ii) Networking and control-plane
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translate into concrete health outcomes. The BRANCH project wants to change that. By combining conceptual work on green space typologies, advanced geospatial analytics, AI-based image analysis, citizen
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integrative part of sustainable and healthy diets. Development of analytical methods, digestion models, dietary modelling, and conducting consumer surveys will form part of the Doctoral Network’s tasks. The 12
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integrative part of sustainable and healthy diets. Development of analytical methods, digestion models, dietary modelling, and conducting consumer surveys will form part of the Doctoral Network’s tasks. The 12
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close collaboration between the named academics at Leeds and York, with the student able to commute as appropriate. They will learn practical aspects of sustainable chemistry at each institution
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years of postdoctoral research experience is preferred. Strong background in big data analytics, machine learning, and multi-omics. Strong track record of high-quality research, demonstrated by