-
Fully-funded 4-year PhD Studentship (UK Home fee status): Numerical simulation of boiling flows for high heat flux fusion components Aim and Objectives This project aims to develop a high-fidelity
-
necessarily require formal education in geotechnics. Applicants with a background in mechanical/materials engineering or alternatively mathematics/computer science with an interest in numerical modelling
-
substantial background in fluid mechanics. Essential skills: Strong knowledge of numerical methods Ability to work effectively in a team Desirable skills / experience: Experience of applying CFD to a complex
-
. Desirable Skills (an advantage, not a requirement) Data analysis skills in python. An interest in energy policy / the economics of energy. Numerical modelling. Eligibility This studentship is available for UK
-
scientific discipline. • First-rate analytical and numerical skills, with a well-rounded academic background. •Demonstrated ability to develop precision mechanical devices/mechatronics •Ability to develop kinematic and
-
are seeking talented candidates with: First or upper second-class degree in Robotics/Mechanical/Cybernetics/Mechatronics /Computer Science or related scientific discipline. First-rate analytical and numerical
-
analytical and numerical skills, with a well-rounded academic background. •Demonstrated ability to develop precision mechanical devices and mechatronics •Ability to develop kinematic and/or dynamic analysis
-
candidates with: ◾First or upper-second class degree in Robotics/Computer Science/Cybernetics/Mechatronics/Mechanical or related scientific discipline ◾First rate analytical and numerical skills, with a well
-
optimal operating conditions and followed by surface analysis techniques (e.g. Scanning electron microscope, X-ray diffraction for residual stress measurements, Electron Back-Scattered Diffraction and
-
in a more accurate analysis of optimizing the service performance. Computer vision approaches such as ones for object identification and action recognition can help to automatically identify deviations