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(i.e. red agents). However, due to a fragmented market, rapid technical developments, and nascent research the extent of capabilities and optimal solution architectures are not well understood. Current
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resource-constrained environments, and it is important to investigate whether features derived from different network layers can be effectively combined. Machine Learning Model Development & Optimization
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project offers a unique opportunity to develop a cutting-edge genomic epidemiology toolkit for real-time fungal surveillance. You’ll optimize DNA extraction protocols using advanced enzyme-based methods
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frameworks to ensure the developed processes are compliant, scalable, and environmentally responsible. Multiobjective optimization algorithms will be employed to balance key performance indicators such as
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; EPSRC Centre for Doctoral Training in Green Industrial Futures | Bath, England | United Kingdom | about 2 months ago
. The research will be computational based, and at this stage is still broad, so we can formulate the optimal plan for the right candidate. We will take an interdisciplinary approach, and you will be able
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-electronic and quantum technologies. What you would be doing: Experimental Design and Execution: Plan, conduct and optimize advanced 4D STEM experiments at cryogenic temperatures. This includes working with
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sensing, and Electromyography (EMG) tools to understand user-device interaction and optimize real-world rehabilitation performance. The student will gain experience in AI, human biomechanics, smart textiles
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modelling tools (CST or HFSS) - Fabricate and test for optimal electromagnetic performance, such as bandwidth, return loss, insertion loss and power-handling. - Develop and characterize new bonding/alignment
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the potential to accelerate materials design and optimization. By leveraging large datasets and complex algorithms, ML models can uncover intricate relationships between composition, processing parameters, and
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become the bottleneck in achieving optimal performance and trustworthiness. This project will focus on how a federated multi-task learning framework can be effectively designed and optimised to address