PhD Studentship: Optimization and Control of Adaptive Aerospace Structures Using Machine Learning (AE0024_v2)
Optimization and control of adaptive aerospace structures using machine learning (AE0024_v2)
Applications are invited for a Ph.D. studentship at the Department of Aeronautics on developing machine learning methods for the optimization and control of adaptive aerospace structures, under the supervision of Dr. Urban Fasel . There will be opportunities for the Ph.D. involved to collaborate with the Reconfigurable & Active Structures Lab at Stanford University and the AI Institute in Dynamic Systems at the University of Washington .
Structures capable of actively changing their shape or reprogramming their mechanical properties, such as morphing wings, deployable space structures, or soft robots, optimize performance by adapting their static and dynamic response to varying operating and environmental conditions. Although the design of such adaptive structures is tightly coupled to their control law, the design and control are usually optimized sequentially. In this project, we will investigate machine learning methods for joint optimization of the design and control of adaptive structures. We will particularly focus on variable stiffness metamaterial structures and exploit their shape and stiffness adaptation properties for active flow control. We aim to establish general design guidelines for variable stiffness metamaterial structures, investigate their applicability to flow control, and develop computationally efficient joint optimization tools for design and control of adaptive structures.
Candidates with a strong background in engineering, computer science, applied mathematics, or a related discipline are encouraged to apply.
Funding, duration, start date, and application deadline
The studentship is available to Home, EU and International students. The position is offered for the duration of 3.5 years, starting as soon as possible. The studentship provides full coverage of tuition fees, a travel budget, and an annual tax-free stipend of £19,668. Applications will be accepted until the position is filled.
For full information on fee status eligibility, please visit https://www.imperial.ac.uk/students/fees-and-funding/tuition-fees/fee-status/
Interested applicants should send a CV, a one-page research statement (interests, goals, and plan), and contact details of two referees to Dr. Urban Fasel (firstname.lastname@example.org ). Suitable candidates will be required to complete an electronic application form, following the Imperial College application procedure: http://www.imperial.ac.uk/study/pg/apply/ . Closing date – until position is filled.
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