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, clinical data and AI-driven modelling for cancer research! In this role, you will bridge the gap between machine learning, computational biology, and haematological oncology. You do not need to arrive as an
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This PhD project, part of the REACT MSCA Doctoral Network, aims to develop an energy-efficient compute-in-memory (CIM) architecture using gain-cell memory for real-time edge learning, addressing
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on the field can be derived from first principles, and how these constraints can improve the technique's performance, particularly when embedded in modern machine learning models. The ultimate goal is to
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. Furthermore, you will collaborate closely with local and international colleagues working in both experimental and theoretical fields to enable you to learn and execute world-leading research. Job requirements
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adipose tissue-derived secreted factors and extracellular vesicles influence cardiomyocyte metabolism and contractility, and on validating newly identified biomarkers using human stem cell-derived
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design and evaluate immersive XR experiences that allow users to safely acquire navigation skills in realistic environments such as public transportation hubs, university campuses, and airports. You will
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design and evaluate immersive XR experiences that allow users to safely acquire navigation skills in realistic environments such as public transportation hubs, university campuses, and airports. You will
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metabolism and contractility, and on validating newly identified biomarkers using human stem cell-derived cardiomyocytes. The project contributes to the development of innovative diagnostic and therapeutic
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improved using machine learning techniques. The developed techniques will be applied to metrology of semiconductor samples. Job requirements You are an enthusiastic candidates with a ‘drive’ for applied
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communication systems, we encourage you to apply—even if you do not meet every item in the project description. We value candidates who are eager to learn, think creatively, and work across disciplinary