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, enabling a more stable and efficacious drug delivery over conventionally dosed medicine. This work integrates high data-density reaction/bioanalysis techniques, laboratory automation & robotics and machine
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and kinematic models with machine-learning-based channel state information (CSI) prediction to enable robust, low-latency connectivity across multi-layer NTN systems. This PhD project sits
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time) as the programme of study You will then need to provide the following information in the ‘Further Details’ section: A ‘Personal Statement’ (include your research interests in flooding and rationale
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code: 8856F Leave the 'Research Area' field blank Select ‘PhD in Process Industries; Net Zero (PINZ)' as the programme of study You will then need to provide the following information in the ‘Further
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depressive symptoms in young people in higher education (YPHE) using ubiquitous smartphone data. Project Description: Mental health challenges, particularly anxiety and depression, are highly prevalent among
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World University Ranking by Subject, it is ranked 20th for Computer Science and Information System, and 21st for Data Science and AI. Edinburgh graduates are ranked 24th in the world by employers
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scientific machine learning-based strategies for the discovery of self-similarity laws, use of quantised local reduced order models, and real data assimilation. You will be assimilated, jointly
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genetics, plant pathology, and computational biology. The student will also spend a minimum of 3 months at KWS UK. The Norwich Research Park Biosciences Doctoral Training Programme (NRPDTP) is offering fully
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is a multidisciplinary research lab, providing the opportunity to develop extensive skills in a breadth of areas, including plant pathology, molecular biology, and computational biology. Whilst
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the laboratory of Dr. Phil Carella, located in the Department of Cell and Developmental Biology at the John Innes Centre. It provides an exciting opportunity to explore disease resistance in the emerging model