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Field
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team of experienced researchers in imaging, machine learning, oncology, and pathology. We do not discriminate on the basis of sex, gender, belief, culture, place of birth or occupational impairment when
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on the development, optimization, and clinical evaluation of new x-ray-based imaging methods. The lab focuses on the use of medical physics approaches to improve image acquisition methods and processing algorithms
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that has been generated in a prior laboratory-setting project. Specifically, we will integrate recent advances in artificial intelligence-based automated interpretation of medical images, and new knowledge
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focus will be on biomechanics, image processing, machine learning (ML), artificial intelligence (AI), and metrology, the student will also contribute to the co-design of cadaver experiments and data
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team and has basic experience in hands-on lab work and in data analysis. Prior experience in optical imaging, nanofabrication, data processing is highly valued but not mandatory. We offer We
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writing skills in English. Knowledge of basic physics of radiation and nuclear decay Desirable Qualifications: Experience in the operation of medical imaging systems, such as PET/CT or SPECT/CT Experience
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crystallization happens in a device and how does this impact power output? How can Magnetic Resonance Imaging (MRI) and X-ray Computed Tomography (CT) be used to visualize and analyze in-situ processes (e.g. phase
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Imaging of Materials Facility (AIM ) led by Professor Richard Johnston and Swansea University's Simulation and Immersive Learning Centre (SUSIM ). The student will develop novel medically bespoke protocols
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lengthy processing times associated with sequencing. This PhD project aims to develop innovative artificial intelligence (AI) methodologies by integrating histopathology images and RNA sequencing data
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linked to exercise limitations and PEM, with the aim of improving patients' quality of life. The PhD candidate will analyze existing data from Harvard Medical School to investigate the connection between