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++, Python, and JavaScript languages, multi- and many-core SoC, RISC-V, hardware synthesis, hardware-software co-design, (meta-heuristic) optimization algorithms, machine learning frameworks, (bonus topics
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considered so far, which are based on the resonating behavior of their meta-atoms and which suffer dissipation losses, the envisioned research will be based on the mutual and external synchronization
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reinforcement learning for large language models (LLMs). Research directions include developing next-generation post-training algorithms, exploring diffusion-based approaches to reasoning with language models
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under uncertainty (Studentship code MSP106) Learning to sample: Meta-optimisation of gradient flows using reinforcement learning (Studentship code MSP107) Dynamic Bayesian modelling of endurance sports
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action discretization, exploration parameters, and decision intervals significantly affects algorithmic convergence and system performance. Distributional RL techniques, such as quantile regression and
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edge AI hardware/software. Contribute to designing and evaluating scheduling algorithms for virtualized or distributed AI resources under varying load, latency, and failure conditions. Build and test a
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/software. Contribute to designing and evaluating scheduling algorithms for virtualized or distributed AI resources under varying load, latency, and failure conditions. Build and test a scenario generator for
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receive training and skills in some of the following: meta-barcoding, stable isotope analysis, trophic-web analysis, Bayesian statistics, wet-lab experimentation – respirometry, fieldwork. Previous
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pathways are connected along extended depth gradients of 0-90 meters. Based in Newcastle, and with international supervision, you will receive training and skills in some of the following: meta-barcoding
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the research. They will conduct relevant systematic and/or meta-analytic reviews to describe the current literature and inform their research. They will undertake qualitative data collection and analysis and use