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for medical imaging, tailored for deep learning. The high-level goal of the project is simple: to use anatomical knowledge and existing knowledge as training data for deep neural networks (instead of manual
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efficient for medicine? If the answer is yes, please continue reading! Join our team! We are looking for a PhD student to work on the topic of shape analysis for medical imaging, tailored for deep learning
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on your background, the work will 1) focus on the interaction between microwave design and measurement methods, looking deep into the technological capabilities of GaN, or 2) focus on new methods
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large-scale neural models of the early visual system. Requirements The successful applicants will have: A solid computational background, an interest in cognitive neuroscience and strong deep learning
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) developing and validating preprocessing pipelines; (3) architecting and comparing spectral-only and multimodal (HSI + NIR + Raman + RGB) deep-learning models; (4) implementing robust sensor-fusion strategies
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particular in deep learning, LLM, digital hardware design, embedded systems, audio processing; Proficiency in deep learning frameworks (e.g. PyTorch) and programming skills (SystemVerilog, Verilog, Python, C
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chemical reaction networks with robotic systems and analytical science. You will also learn how to programme robotic systems and how to implement aspects of deep learning and neural networks for reservoir
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PhD: A deep dive into youth cyberhate Faculty: Faculty of Social and Behavioural Sciences Department: Education & Pedagogy Hours per week: 28 to 40 Application deadline: 5 September 2025 Apply
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. For this PhD position you will work on modelling the processes and feedbacks that couple the AMOC and polar ice sheets, with particular focus on sea ice and (North Atlantic) deep-water formation regions such as
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models. Requirements The successful applicants will have: A solid computational background, an interest in cognitive neuroscience a and strong deep learning programming skills. Ability to work in an