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revolution in generative AI has been driven by the rapid growth in training and inference compute for Foundation Models (FMs). This scaling paradigm, however, is characterised by high energy demand, high
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experimental or prototype systems, including material and energy balances, process flow diagrams, piping and instrumentation diagrams, and equipment selection. A strong understanding of electrochemical
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work under the supervision of the Principal Investigators and/or higher-level research staff. He/she will be required to conduct in vivo and in vitro experiments, collect and process tumor samples
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aspects of the position include chronic microelectrode implantation surgery, neural recording (electrophysiological and imaging), calcium imaging of electrically stimulated ex vivo retina, behavioral
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advancing new molecular imaging and therapeutic agents from bench discovery toward preclinical and clinical application. The laboratory is focused on novel agent development, in vitro and in vivo development
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funding opportunities to build research independence. Key skills and experience required: A PhD in in physiology, exercise physiology, cardiovascular science, biomedical science or a related discipline
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developing an AI-based digital twin to generate mechanistic targets and design therapeutic molecules using a dynamic prototype. IROC has generated datasets on mechanistic targets that need to be studied in
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to analyze complex image and real-world datasets and identify clinically and psychologically relevant patterns Lead independent scientific research projects covering study design, data processing, statistical
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related areas attract external funding. About you a PhD (or near completion) in statistics or a closely related area evidence of independent research, compatible with the standards at The University
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colorectal tumors. Specifically, the work will focus on optimizing 3D X-ray virtual histology methods by phase-contrast micro-CT and corresponding image processing techniques also with view on combining it