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acquired and will also be determined by the funding available. About you You should hold a PhD/DPhil, or be close to completion, of a relevant PhD/DPhil with a proven record of being able to learn and apply
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civil society. Upholding best practice in research conduct, ethics, and data management. Person Specification You will hold (or be very close to completing) a PhD in a relevant field such as sound studies
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, to the rigorous study of quantum many-body problems. The successful candidate is able to actively contribute to the research profile of the Faculty of Physics, teach courses (max. 4 hours/week per semester), and
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will be educated to PhD level with relevant experience in molecular plant biology and evolution and will work closely with other group members to assist them with gene functional characterisation
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this role, we are looking for candidates to have the following skills and experience: Essential criteria PhD qualified in relevant subject area* Experience developing deep learning segmentation models
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experience and ambitions Required Qualifications PhD in Electrical Engineering, Physics, Photonics, Materials Science, or related field Strong theoretical and practical background in semiconductor device
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learning. The post-holder will be familiar with the use of these techniques and experience of dataset construction and data mining will be essential. The successful applicant will have completed an MPhil/PhD
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scientific publications, patents, and seeing collaborators translate our work into real-world settings. You will be responsible for developing machine learning and AI algorithms for a range of data and
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), to develop systems that improve the efficacy of machine learning-based technologies for healthcare applications. You must hold a PhD (or be near completion) in a field such as AI, computer science, signal
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-quality robotics research in the areas of robot grasping and manipulation, kinematics and mechanisms, sensing, and human-robot interaction. Within CORE, SAIR focuses on multimodal machine learning for human