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. Research will be focused on the lifelong safety assurance of increasingly autonomous AI systems in dynamic and uncertain contexts. It will build on methodologies and concepts in disciplines spanning AI
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) of high-value critical assets. Through this PhD research, algorithms and tools will be further improved and developed, validated and tested. It is expected that combining the domain knowledge and the
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PhD Studentship: Improved Heat Transfer Understanding via Conjugate Heat Transfer, Co-Simulation and AI Approaches Research has shown that the development of gas turbines is critical to the success
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/or dynamic analysis of mechanical/robotic systems •Ability to use finite element modelling and to simulate complex mechatronics •Ability to implement control and kinematics with hardware-in-the-loop
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4-year D.Phil. studentship Supervisors: Dr Simone Falco, Prof Daniel Eakins The ability to simulate initiation and detonation effects within energetic materials is a significant capability gap
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representing curved surfaces with piecewise linear approximations. The error introduced by using FE is particularly limiting when modelling dynamic events, as numerical dispersion and dissipation error of waves
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its environment and respond optimally in dynamic operating conditions. Meanwhile, you will also develop intelligent control strategies that minimise energy use while ensuring punctuality and safety
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precision Mechatronics systems and algorithms. Ability to develop kinematic and/or dynamic analysis of Mechanical/Robotic systems. Ability to implement control and kinematics with hardware-in-the–loop
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modelling of the gut microbiome. This is a unique opportunity to work within a dynamic, multidisciplinary team of microbiologists, clinicians, and molecular scientists with a strong track record in
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. Experience with molecular dynamics software such as LAMMS is desirable. Experience with molecular simulation software is beneficial. To apply please contact Dr Siperstein - flor.siperstein@manchester.ac.uk