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focusing on multi-omic integration analytics, machine learning, and/or AI. In addition to carrying out research, the successful candidate will be expected to apply for fellowship funding, contribute
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functional theory. - Effective Hamiltonian methods for quantum phenomena in solids. - Development of machine learning tools for topological materials. - Experimental studies of magnetotransport in quantum
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and modelling of omics, clinical and imaging data, development of reproducible pipelines, application of machine learning techniques, integration of multi-modal data, scientific publication and
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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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*• Experience in Python or another programming language (projects, GitHub repositories, courses, scientific use).• Training or experience in machine learning and data science applied to environmental or energy
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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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of: • machine learning • cybersecurity • distributed systems • privacy-enhancing technologies The research will be carried out within the (team name) at LS2N, focusing on trustworthy AI and cybersecurity
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-FNR PEARL Research Grant in the area of Information Systems Engineering, and, depending on interest, in fields, such as Generative AI & Machine Learning, Data Privacy, Cyber Security, Digital Identities
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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