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. The studentship is funded by the Leverhulme Trust through the Connected Waters Leverhulme Doctoral Programme, which is supporting new research on human-environment interactions in freshwater ecosystems. There is an
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detection of chemical and microbial contaminants in rivers. The studentship is funded by the Leverhulme Trust through the Connected Waters Leverhulme Doctoral Programme, which is supporting new research
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candidate would have experience with computational modelling and control of dynamical systems. Other useful skills include scientific programming (e.g., Python or Matlab), control system design, and
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strengths and interests (e.g. geospatial data science or socio-environmental modelling). Funding Sponsored by the Leverhulme Trust and Cranfield University, this Connected Waters Leverhulme Doctoral programme
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covers fees and stipend for a home (UK) student with funding provided by the Leverhulme Trust through the Connected Waters Leverhulme Doctoral Programme. Options exist for PhD and Master + PhD routes
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you with a highly sought-after interdisciplinary skillset bridging ecological theory, modelling, field ecology, agricultural systems and applied environmental science. As a CASE-supported project with
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be interested in applying social science approaches to an environmental setting. However, we welcome applicants from a range of disciplines and experiences, who have a passion for environmental
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, environmental science, urban sustainability, geospatial analysis, or quantitative modelling. We particularly welcome applicants who are excited about integrating ecological understanding with data-driven methods
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: • Experience with programming (Python, MATLAB), • background in aerospace, computer science, robotics, or electrical engineering graduates, • hands on skills in implementation of fusion
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AI techniques for damage analysis in advanced composite materials due to high velocity impacts - PhD
intelligence, particularly in computer vision and deep learning, offer an opportunity to automate and enhance damage assessment by learning patterns from multimodal data. This research seeks to bridge the gap