64 machine-learning-"https:" "https:" "https:" "https:" "https:" "https:" "UCL" positions at Cranfield University
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the intersection of ecology, machine learning, and sustainable land management, the research will combine field data collection, deep learning model development, and stakeholder co-design to support biodiversity
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that equipment’s or machine components are maintained on these schedules even without a need of repair or replacement. In addition, the stoppages to execute the schedules strategies also increase the production cost
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at leading international conferences and publish in top-tier journals. The successful candidate will gain advanced expertise in multi-sensor fusion, signal processing, machine learning, and positioning
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systems safer, more efficient, and more sustainable. The aim of this project is to design a smart cognitive navigation framework that information from various sensors and learn to make decisions on its own
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aircraft, utilized for research into thermal management and system health monitoring, supporting studies in military aircraft systems. Engaging with these facilities allows students to acquire practical
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leaders in technology and management globally. Learn more about Cranfield and our unique impact here . Our Values and Commitments Our shared, stated values help to define who we are and underpin everything
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, Cranfield’s world-class expertise, large-scale facilities and unrivalled industry partnerships are creating leaders in technology and management globally. Learn more about Cranfield and our unique impact here
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their potential via apprenticeships. You will champion the use of technology to underpin and evidence the apprentices learning and support the journey towards successful apprenticeship completion. About You You
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institutions. State-of-the-art facilities: Access advanced laboratories and high-performance resources. Flexible learning: Tailored research projects aligned to personal interests and career aspirations. Career
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validation with end-users. The student will have access to specialised training in quantum security and advanced machine learning. The self-funded nature of the project affords the unique flexibility to pursue