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should hold a Honour first degree and/or a Master’s degree in Mechanical Engineering, Control Systems, Mechatronics, or a closely related discipline. The ideal candidate will have strong skills in MATLAB
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neuroscience and data analysis Proficiency in programming (e.g., Python, MATLAB, and similar languages) Experience with large-scale neural network simulations Experience with analysing large-scale neural
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with coding, ideally in Python or MATLAB Funding support This studentship is open to Home students only. It is jointly supported by the Faculty of Engineering and industrial partners which is expected
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or magnetometry • Numerical and analytical approaches (e.g., MATLAB, Mathematica, etc.) Training and resources Modelling component: The successful candidate will develop several computational skills in terms
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techniques (e.g. StarCCM+, OpenFoam, Matlab, LabView, Rhino, PIV, openwater, cavitation, noise tests.). Prior research experience in propeller, wind/tidal turbine design and optimisation is highly desirable
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: • Experience with programming (Python, MATLAB), • background in aerospace, computer science, robotics, or electrical engineering graduates, • hands on skills in implementation of fusion
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, training, and collaboration Preferably a strong background in aircraft design and propulsion systems Preferably Proficient in programming (MATLAB preferred; Python is also acceptable) Prior experience with
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•Background with relevant packages, (CREO/SOLIDWORKS, ANSYS/ABAQUS, MATLAB) •A driven, professional and self-dependent work attitude is essential •Experience of working within the industry will be an advantage
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monitoring. Familiarity with tools such as Python, MATLAB, or embedded C would be advantageous. Most importantly, this project is ideal for applicants who are motivated to tackle real-world reliability
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/robotic systems Ability to implement control and kinematics with hardware-in-the–loop Background with relevant packages, (MATLAB, SolidWorks/Creo, ROS/ OpenCV/ python, LabVIEW/C languages) A driven