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strategic priorities in Digital Twins, Environmental Intelligence, and Data-Driven Engineering, using advanced computational modelling to support ecosystem resilience and sustainable management. The project’s
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This is an exciting PhD opportunity to develop innovative AI and computer vision tools to automate the identification and monitoring of UK pollinators from images and videos. Working at
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Computer Science, Electrical Engineering, Cybersecurity, or a related discipline. Strong programming skills (Python or similar). Knowledge of AI/ML concepts and techniques. Understanding of cybersecurity
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research and strong written and oral communication skills, as we can complete existing strengths with targeted training. Funding This fully funded Connected Waters Leverhulme Doctoral programme studentship
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CDT and programme can be found at AMR CDT webiste Applications should be submitted by 12th January 2026.
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Engineering Solutions for Antimicrobial Resistance. Further details about the CDT and programme can be found at AMR CDT webiste Applications should be submitted by 12th January 2026.
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for Doctoral Training in Engineering Solutions for Antimicrobial Resistance. Further details about the CDT and programme can be found at AMR CDT webiste Applications should be submitted by 12th January 2026.
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characterisation is advantageous How to apply This project is offered as part of the Centre for Doctoral Training in Engineering Solutions for Antimicrobial Resistance. Further details about the CDT and programme
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for Antimicrobial Resistance. Further details about the CDT and programme can be found at AMR CDT webiste Applications should be submitted by 12th January 2026.
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Solutions for Antimicrobial Resistance. Further details about the CDT and programme can be found at AMR CDT webiste Applications should be submitted by 12th January 2026.