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Applications are invited for an EPSRC Industrial Doctoral Landscape Awards (IDLA) PhD position at the University of Nottingham addressing the specific engineering details of the wear of articulating
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education. If you think this position is relevant and interesting, we encourage you to apply, regardless of gender, functional ability and cultural background, or whether you have been out of work for a
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graduate school. As part of your PhD position, you will have a 10% teaching task and the opportunity to work towards the University Teaching Qualification. Besides creating scientific papers based on your
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facilities, including 7T MRI and the Netherlands Brain Bank. You will work in a diverse, interdisciplinary community of cognitive and clinical neuroscientists and neuroimaging experts. It concerns a position
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PhD Scholarship in Development of Cement-Free Living Building Materials for Sustainable Construction
value through the natural and technical sciences for the benefit of society. DTU has 13,800 students, 1,600 PhD students, and 6,500 employees. We work in an international environment and have an inclusive
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equivalent knowledge in some other way in Sweden or abroad. The candidate must have: very good oral and written proficiency in English. Highly motivated and able to work independently and as a part of a large
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acquire in the course of the PhD skills in live microscopy, experimental neurobiology, genetics and behavioural work. The student will receive mentoring and have the chance to guide the research and
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remained unclear for decades, and thus design principles for discovering new thermal battery materials have been lacking. In this industrially sponsored PhD studentship, we have partnered with AWE fund a 3.5
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equally good academic foundation. If you have a weaker academic background, you may be considered if you can document that you are exceptionally suited for a PhD education, for example through relevant work
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, navigation in waves or ocean currents, and underwater manipulation. At the same time, Foundation Models have transformed several fields of artificial intelligence by learning general-purpose representations