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Field
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energy; thereby minimising farming’s environmental impact. AI machine learning offers a new expedient method of developing control systems for tasks that would be difficult to manage using classical
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. The system will leverage cutting-edge techniques in Natural Language Processing (NLP), Machine Learning (ML), and Multimodal Analysis to conduct adaptive interviews, assess candidate responses, and generate
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This exciting opportunity is based within the Power Electronics, Machines and Control (PEMC) and Composites Research Groups at the Faculty of Engineering, which conduct cutting-edge research
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which there exists extensive experience in the areas of machine learning, biostatistics, and medicine: Dr Yanda Meng and Dr Tianjin Huang (Machine Learning), Prof Yalin Zheng (AI in Healthcare), A/Prof
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/or modelling is essential. Experience in machine learning, computer vision, and computer programming is desirable. In addition, applicants should be highly motivated, able to work independently, as
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operational data and machine learning. You will be based at UCL mechanical Engineering, and collaborate with industry and port partners on system design, prototyping, and lab-based trials. Key responsibilities
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+to+apply#Howtoapply-Eligibility) a Master’s degree in Artificial Intelligence, Machine Learning, Computer Science, Cognitive Science, Psychology or a related field excellent knowledge in AI and at least one
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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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from visual and auditory cortices recorded over multiple days Apply and adapt advanced machine learning frameworks (SPARKS and CEBRA) for supervised and unsupervised analysis of high-dimensional neural
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, at least a 2.1 honours degree or a master’s (or international equivalent) in a relevant science or engineering related discipline. Strong background/skills on machine learning, mathematics, probabilistic