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: research experience in skin biology, tissue repair, reparative medicine, epigenetics, or RNA biology experience in multi-omics integration, advanced statistics, machine learning, or biological data
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diagnosis of gas turbines. The project focuses on developing an integrated approach that combines machine learning techniques with physics-based models to estimate the health of various system components
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. The project focuses on developing an integrated approach that combines machine learning techniques with physics-based models to estimate the health of various system components. The aim is that fault diagnosis
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. Teaching skills. Additional assessment criteria: A strong ability to develop and conduct high-quality research independently. Experience using deep learning methods and computer vision with biological data
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year. You should have knowledge and experience in bridging quantum and classical machine learning, and be fluent in English, both written and spoken. Assesment criteria Qualifications that are considered
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will also use focussed ion beam milling scanning electron microscopy (FIB-SEM) to prepare infected cells for in situ cryo-ET. The resulting tomographic data will be analysed by machine-learning assisted
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supervision The following experience will strengthen your application: Advanced coding skills (Python, R, etc.) Expertise in GIS and data visualisation. Experience applying Machine Learning, particularly
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disorder. This project investigates early neural markers of psychosis by integrating multimodal neuroimaging with genetic and transcriptomic data and applying machine-learning approaches to identify
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conduct research on the theoretical foundations of mathematical optimization, as well as its applications to emerging challenges in machine learning and engineering. You will write and submit research
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The applicant must: hold a PhD in a relevant field (e.g. computer science, artificial intelligence, machine learning, computer vision, animal science, biology, veterinary medicine, or a related discipline) have