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Field
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Embark on a ground-breaking PhD project harnessing the power of Myopic Mean Field Games (MFG) and Multi-Agent Reinforced Learning (MARL) to delve into the dynamic world of evolving cyber-physical
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personal interest in experimental aerodynamics/aeroacoustics and environmentally sustainable technology is highly valued. Skills in data analysis and flow physics interpretation are essential. Ability
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ability to think and work independently. Excellent Analytical skills and strong verbal and written communication skills are also essential requirements. A Masters qualification is not required if you have a
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combination of academic and industrial challenges which will enhance the student’s ability to tackle complex intellectual and practical aspects of computer vision and robotics. We are seeking talented
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holiday and closure days (pro rata) with the ability to purchase more. Flexible working opportunities, including hybrid working for some roles. Generous pension scheme. A wide range of discounts and rewards
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of mechanical and robotic systems •Ability to use finite element modelling and to simulate complex mechatronics •Ability to implement control and kinematics with hardware-in-the–loop •Background with relevant
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This 4 year PhD project is fully funded and home students, and EU students with settled status, are eligible to apply. The successful candidate will received an annual tax free stipend set at
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independently or in teams within and outside the university, with excellent organization, communication and time-management skills. In particular, their ability to independently think and formulate solutions
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Business School. The lab brings together researchers working across a wide range of empirical fields, including: Labour economics Macroeconomics Sports economics Political economy Consumer economics
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Fully-funded PhD Studentship: Adaptive Mesh Refinement for More Efficient Predictions of Wall Boiling Bubble Dynamics This exciting opportunity is based within the Fluids and Thermal Engineering