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conferences, expanding your network and gaining valuable experience in scientific communication. Supervisors: Dr Isabella Guido and Dr Richard Sear Entry requirements Open to UK nationals only. Starting in
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; EPSRC Centre for Doctoral Training in Green Industrial Futures | Bath, England | United Kingdom | 3 months ago
support UK industry to reach net zero. Alongside their research, our PhD researchers gain valuable training in how to apply their research within the wider industrial system, including opportunities
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enable cohesive analysis across modalities and length scales. This PhD project is a collaboration between Swansea University and Carl Zeiss, a global leader in microscopy solutions. Both partners
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project would suit students with a background in electronics, embedded programming, signal processing, vibration measurement and analysis, maintenance engineering, and electro-mechanical engineering
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for microscale analysis and a strong tradition in analytical and electroanalytical chemistry. Loughborough University has an applied research culture. In REF 2021, 94% of the work submitted was judged to be top
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, Biomedicine, or Computational Systems Biology. Experience with NGS data or single cell-sequencing analysis is required. Competence in adipose tissue biology, cell culture work or mouse physiology would be
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: Framework Development: Design and implement a generative deep learning framework for cross-modal integration and analysis, resilient to distribution shifts. Correlation Discovery: Identify interpretable
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methods for analyses of large-scale genomic data such as genome-wide analyses, polygenic prediction, Mendelian randomization, functional bioinformatical follow up and network analysis. The project will
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the demographic profiles and contact networks of the individual simulation model will be drawn from Virtual WA: a geospatial analysis platform built in-house by A/Prof Cameron and Camilo Vargas at The Kids. Student
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techniques from optimization and control theory, scientific machine learning, and partial differential equations to create a new approach for data-driven analysis of fluid flows. The successful applicant will