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proteins. Moreover, you will determine whether the success of such alternations depends on protein family and on mRNA characteristics such as codon optimality. You will construct a panel of engineered cell
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including number theory, dynamical systems, probability theory, equidistribution theory and optimal transport. The aim of the PhD project is to develop a flexible framework based on harmonic analysis to study
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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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; implementation of digital twins that enable real-time decision optimization; and establishment of cross-industry frameworks that allow technology transfer between sectors. Your findings will be published in high
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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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on Artificial Intelligence (AI), Deep Reinforcement Learning (DRL), and Predictive Maintenance for optimizing wind turbine performance and reliability. This research will develop an AI-powered wind turbine
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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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. The research group is seeking a talented Doctoral Researcher in nonlinear systems and control with strong interest in nonlinear stability theory, modeling & identification, optimal control, certifiably safe
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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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optimization. An ideal candidate is expected to have a strong interest in theoretical and innovative research. To apply. Please contact the supervisor, Dr Chao Chen - chao.chen@manchester.ac.uk . Please include