619 computational-physics "https:" "https:" "https:" "https:" "U.S" PhD positions in United Kingdom
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prior to obtaining their visa and to study on this programme. How To Apply You must apply through the University’s Apply to Newcastle Portal Once registered select ‘Create a Postgraduate Application’. Use
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. The candidate should have a good 2.1 Bachelors, or Masters degree in Electronic Engineering, Computer Sciences or equivalent. Experience in communications and networking, AI, or robotics is desirable but not
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autonomous systems. You will have the freedom to shape the project direction, experiment with novel ideas, access state-of-the-art AI and computing facilities, receive tailored training and professional
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of the advertised projects, contact the relevant supervisory team. If it is more general, contact the programme or one of our Directors . You can also contact us at any stage later in the process. When you are ready
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supervisor - Dr Haitao Lan, if you have any questions. Entry requirements: Applicants should have, or expect to achieve, a first-class / high 2:1 degree in a relevant Engineering or Physical Science discipline
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. The project focuses on power-aware computing, thermal optimization, and sustainable electronic design, targeting critical applications in aerospace, healthcare, and industrial automation. Hosted by the renowned
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integrating machine learning, computational modelling, and experimental validation. The successful candidate will receive training in both computational and experimental biology within a highly collaborative
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, Chemistry, Physics, Engineering, Mathematics, Computer Science, Data Science, Machine Learning or Artificial Intelligence a minimum 2:1 undergraduate degree (or equivalent) Excellent spoken and written
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necessarily require formal education in geotechnics. Applicants with a background in mechanical/materials engineering or alternatively mathematics/computer science with an interest in numerical modelling
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, embedded intelligence, and adaptive cyber-physical systems that operate safely under uncertainty and dynamic conditions. This PhD at Cranfield University explores the development of resilient, AI-enabled