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Learning Centre; a complete educational program for PhD students; multiple courses on topics such as leadership for academic staff; multiple courses on topics such as time management, handling stress and an
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or international equivalent, in a subject relating to neuroscience or neural engineering. Further qualifications such as an MRes is advantageous. The candidate must have or be willing to learn computational methods
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domains are e.g., signal-/image processing, artificial intelligence and machine learning. Tasks: research and development in designing and programming field programmable gate arrays (FPGAs) for accelerating
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realistic impairments in terms of hardware, propagation environment, and sensing imperfections. AirComp is a novel technique that harnesses the interference in wireless multiple access channels
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writing skills. o Proactive mindset and ability to work in a multidisciplinary and collaborative environment. o Adaptability and openness to learn new tools and methods. Language skills
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materials systems at the molecular level with machine learning. The PhD Student will work with tumour sections to develop multiple instance learning and weak supervision / spatial transcriptomics models
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machine learning for next-generation wireless networks, (ii) Foundations of semantic communications and age of information, (iii) Stochastic geometry and spatial modeling of large-scale wireless systems
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data to answer relevant questions and solve real-world problems. It brings together fundamental, methodologically driven research in optimization, machine learning, and artificial intelligence with
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Job Description Are you interested in developing novel machine learning methodologies that are scalable, reliable and explainable and that can address imminent challenges? Responsibilities and
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Robotisation (PROMAR) group, headed by Matthias Rupp. The group develops fundamental and technological expertise in machine learning for materials science, including data-driven accelerated simulations and