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conducted in collaboration between Linköping University (LiU) and Lund University (LU). Read more here: https://elliit.se/project/machine-learning-for-sensing-in-distributed-wireless-systems/ Distributed MIMO
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or more of the following areas: AI and machine learning, natural language processing, large language models (LLM), experience in designing prompts, fine-tuning LLMs, or distributed systems. Good knowledge
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of senior researchers, the individual will help apply machine learning methods, with a focus on reinforcement learning, to mathematical problem solving. The role emphasizes hands-on experimentation
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Research and Imaging Centre (BRIC; https://www.plymouth.ac.uk/research/psychology/brain-research-and-imaging-centre ) . TARAs benefit from waived study fees and will work towards a PhD in an area of
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researchers and PhD students. The research groups conduct research in various areas of mobile network systems, multimedia and AR/VR/XR systems, robotics and machine learning, focusing on fundamental aspects as
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collaboration with Dr Whelan and the PhD students, machine learning tools for the handling of the Mauve and MUSE datasets. They will also be expected to lead the research into innovative ways in which the machine
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, energy consumption, and packet loss. The use of distributed machine learning provides a relevant solution to mitigate the lack of communication reliability [3][4]. This PhD proposes to guide the learning
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Application Restrictions Open to both Internal and external Job Type Open Learning Faculty Member Posting In effect from 19/3/2026 Closing Application Date 26/3/2026 OLFM Type Regular Continuous
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thesis is not pre-defined and should be defined by the candidate over the course of the first year. The domain of the PhD thesis must be machine learning and either control theory, path planning, or multi
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following areas: Strong foundation in machine learning, optimization, and deep learning algorithms, including Transformer architectures. Hands-on experience or solid theoretical knowledge of LLMs/SLMs