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A 3.5 year PhD Engineering studentship under the supervision of Dr Evgeny Petrov . Blade vibrations in compressor and turbine bladed disks are one of the critical issues in designing gas-turbine
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-enhanced exact methods, particularly focusing on Column Generation (and Branch-and-Price), to improve scalability and convergence in solving complex optimization problems. In collaboration with your
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farm layouts with high specific power density through the co-optimization of array design and control strategies. This project is a collaboration with CNR-INM Institute of Marine Engineering in Rome
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(HTPB) and isocyanates for optimization of formulation (pot life) and product mechanical properties for application in solid rocket propellants. Due to the confidential and commercially sensitive nature
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Discipline: Engineering & Technology, Materials Science, Mechanical Engineering Qualification: Doctor of Philosophy in Engineering (PhD) This collaborative project between Jaguar Land Rover (JLR
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harness advanced techniques such as machine learning, optimization algorithms, and sensitivity analysis to automate and enhance the mode selection process. The result will be a scalable methodology that
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Research Assistant/Associate in Photonics Integration of Graphene and Related Materials (Fixed Term)
investigate the large area production of graphene, BN, MoS2 and other layered materials, optimize their transfer process in view of their application in energy, electronics and photonics. This will include
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combination of education and experience Position Description: The University of Louisville Chemical Engineering Department is seeking a Temporary Engineering Senior Researcher. We are seeking a highly skilled
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- looking. Sensor technology plays a key role in this transformation, enabling real-time monitoring, automation, and intelligent decision-making. Despite these needs, many water treatment processes still rely
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Engineering, and Engineering Management. Students with interests in computational mechanics, optimization design, bioinspired design, sustainability management, machine learning, AI, uncertainty quantification