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for a full-time lecturer position. The appointment will be effective August 2026. Successful candidates must be able to teach in-person human anatomy and physiology lectures and labs. Candidates should
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profile and an interest in developing new AI models for high-dimensional biological data. You should have a solid foundation in areas such as machine learning, applied mathematics, statistics
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AI. Candidates must possess substantial experience in artificial intelligence and machine learning methods, specifically in AI-driven materials discovery, machine learning applications for materials
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validate adaptive mechanisms for LoRaWAN based on machine learning techniques, targeting improved reliability and energy efficiency in mobile scenarios. To achieve this, it is necessary to go beyond
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will apply machine learning — in particular physics-constrained symbolic regression — to discover compact analytical spin-Hamiltonians and their parameter dependencies. These Hamiltonians will feed large
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of national and/or regional significance in the arts and sciences, business, humanities, education, and health and human services, a specialist degree in instructional technology, and PhD degrees in marine
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: MSc degree completed. Additional optional skills and qualifications: Previous research experience, particularly in the fields of Internet of Things security and machine learning models applied
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analysis Background in biomedicine and digital pathology What we offer Embedding within a computational team, with extensive experience in computational biology and machine learning. Embedding within
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Artificial intelligence and machine learning methods for model discovery in the social sciences School of Electrical and Electronic Engineering PhD Research Project Self Funded Prof Robin Purshouse
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/359354/2-phd-positions-on-learning-cau… Requirements Additional Information Website for additional job details https://www.academictransfer.com/359354/ Work Location(s) Number of offers available2Company