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Field
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clear aptitude for research. You will have a Masters in a relevant discipline and will definitely have a 1st or 2:1 Bachelors degree (or equivalent). You will be able to demonstrate excellent mathematical
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equivalent) in a science, mathematics or engineering discipline. Applicants without a master's qualification may be considered on an exceptional basis, provided they hold a first-class undergraduate degree
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interpersonal skills with the ability to work with patients, members of the public, and present as a representative of the lab at national and international scientific events • Demonstrate strong mathematical
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salary) for 3 years An additional £2,000 per annum for consumables and travel Requirements The candidate should have a 1st or high 2:1 degree in electrical/mechanical engineering, physics, mathematics
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, mathematics, or related disciplines. Skills in numerical tools and programming are desirable. Any experience in engineering design or manufacturing would be advantageous. Eligibility and Application Due
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. Ideal Candidate: You’re a highly motivated student with a first-class or upper second-class honours degree (or equivalent) in Engineering, Physics, or Applied Mathematics, or a related field. Strong
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. Successful candidates should have a background in applied mathematics/theoretical physics/computer science or related discipline, alongside strong computational skills. Value: Home fees paid and stipend
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) at the master’s level (at least a 2.1 honours) in a relevant science, mathematics, or engineering discipline are especially encouraged to apply. Additional requirements: Demonstrated determination and resilience
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strongrecord of scientific productivity is preferable. The successful applicant must have a Ph.D.in Computer Science, Data Science, Statistics, Mathematics, Physics, Engineering orrelated field at the start of
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Science, Statistics, Applied Mathematics, or a related field. • Strong background in convex analysis, statistical machine learning (reinforcement learning, LLM and generative modeling), stochastic modeling and