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Field
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industrial processes. Your research will drive a paradigm shift in how TES systems are modelled, integrated, and controlled within industrial settings. You will develop novel, adaptive, physics-informed models
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Systems, or a related field. Strong analytical and critical thinking skills. Strong machine learning (ML), computer vision (CV), large language models (LLM) for quantitative data, texts, images, and sensor
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for high-energy X-rays, thermal instability, or issues related to toxicity and cost. As imaging demands increase—particularly in fields like high-throughput synchrotron science, medical diagnostics, and
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, single-molecule techniques, and optical imaging is welcomed. We foster diversity and female candidates are particularly invited to apply, since the gender balance recently declined with the departure
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, or a related discipline. Hands-on research experience in one or more of the following areas will be considered an advantage: Confocal microscopy and Image processing Optical bench instrumentation
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Funding for: UK/Home Students We invite applications for a fully funded PhD research scholarship in “Unsupervised Machine Learning for Cardiovascular Image Analysis”. This opportunity is available
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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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, engineering, bioinformatics, machine learning, artificial intelligence) to support minimally invasive and targeted preventive and predictive medicine capable of limiting age-related functional disorders