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, energy principles, and gradient-flow techniques can be used to analyse the existence, regularity, stability, and long-time behaviour of solutions to kinetic PDEs. The project aims to develop rigorous
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This PhD project will develop mathematical models to investigate population dynamics in biological systems. Combining dynamical systems theory, mathematical modelling, and data-driven approaches
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(NextGen-Fi). The post will contribute to the development and validation of unique Unpiloted Airborne Vehicle [UAV] datasets. The work will focus on UAV data management, orthophoto optimisation and
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AI and data science, particularly in dynamic settings where observations are collected sequentially and decisions influence future outcomes. This project will develop novel machine learning and
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. The objectives are as follows: Develop a novel centrifuge testing programme to measure the thermomechanical response of energy piles under complex loads, which will include VHM and tensile loading. Develop a
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tissue. Career development: scientific communication, project management, IP/translation, and outreach—core components of MSCA ITN training. Who Should Apply We seek a highly motivated candidate with
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the work aligns with your goals and aspirations. For more details see https://binyshlab.com/positions/ What will you do? Conventional robotic bodies rely on computationally intensive centralized control and
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desk-based population viability analysis. Improved understanding of habitat preferences will help identify, prepare and manage future translocation sites, support existing sites, and provide insight
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cells with tailor-made error-correcting codes. The successful candidate will combine computational design with laboratory work, developing DNA/RNA systems, molecular circuits, and experimental assays
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manufacturing processes as well as energy storage. This is a diverse team that values collaboration, scientific excellence and research integrity. The primary role of the researcher is to develop new scalable