Junior Research Associates
Junior Research Associate Scheme 2025
Applications for the JRA scheme are open from Thursday 6th February until Monday 17th March 2025.
The JRA scheme endeavours to benefit all students who are enthusiastic about a future in research, and who will conduct a successful research project with merit. For further information on the scheme, eligbility and how to apply visit the JRA webpages.
Students can either apply with one of the projects below or suggest an original proposal. Applicants must be sponsored by a member of faculty so please contact the project supervisor to dicuss the project and application. Visit our website for a full list of our Engineering faculty.
- Multi-phase flows for future energy systems
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Supervisor: Dr Martin T White (martin.white@sussex.ac.uk)
The expansion and compression of multi-phase fluids if relevant to a range of future net-zero energy technologies including power generation cycles and heat pumps. At Sussex we are currently building a new experimental test facility to study these processes a fundamental level. In this JRA project, you will have the opportunity to support in the design and development of a new experimental atomisation nozzle to generate controllable multi-phase flow conditions within the test rig. There will opportunities to work on developing CAD and numerical simulation skills, as well hands-on skills by supporting the commissioning of the test facility.
- Heat pumps for industrial energy decarbonisation
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Supervisor: Dr Martin T White (martin.white@sussex.ac.uk)
Industrial heat decarbonisation is a significant challenge that requires urgent action to meet net-zero targets. Within this remit, high-temperature industrial heat pumps are expected to be critical but are not yet widely available. In this JRA project, you will have the opportunity to contribute to leading research at Sussex that is exploring innovations in heat pump cycle design. You will work on developing a mathematical model of an industrial heat pump system that can be used to identify optimal systems for different industrial application. There will the opportunity to develop critical research skills in modelling energy systems and using computational software such as MATLAB and Simulink.
- Characterization of a continuous variable transmission mechanism based robotic joint
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Supervisor: Dr Rizuwana Parween (r.parween@sussex.ac.uk), Dr Nicolas Herzig (n.f.herzig@sussex.ac.uk)
For navigating an unknown and unstructured environment, robots usually employ active impedance control methods for enhancing collision safety performance. However, due to high radiation in nuclear environments, it is difficult to employ a lot of sensors as in traditional robotic systems as the radiation depletes the performance of sensors in prolonged exposure. Continuous variable transmission (CVT) mechanism-based robotic joint would provide safe collision in such an environment by mechanical means, minimising the requirement of sensor feedback mechanism t an in-house test rig to explore and characterise the collision-safe performance of the CVT. The Junior Research Associate will have the opportunity to contribute to the design and characterisation of CVT and the test rig. It will be an opportunity to develop hands-on skills, including fabricating robotic and mechatronic systems and collecting and analyzing data from characterisation tests.
- Learning for the Detection and Tracking of Transcritical Droplets
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Supervisor: Prof Cyril Crua (C.Crua@sussex.ac.uk)
This JRA project hopes to develop and rigorously validate Deep Learning (DL) tools to assist in researching the chemical properties of Sustainable Aviation Fuels (SAFs), such that we may better understand their injection, combustion, and emission processes to contribute to decarbonising the aviation industry. You will use high-speed long-distance microscopy videos of SAFs undergoing the transcritical mixing process (transition from liquid fuel to supercritical state) and computer vision techniques to characterise three regimes of droplets using state-of-the-art detection and tracking DL algorithms. In time, we believe these tools could better capture, quantify, and mathematically model spray development.