Shahab Resalati

[Bursary of £20,780 per annum for 3 years. University fees at the home rate are covered. International and EU students without Settled Status must pay the difference between home and international fees. Visa and associated costs are not covered.] PhD Studentship: AI-Enhanced Battery State of Health Estimation Using Ring Probabilistic Logic Neural Networks Oxford Brookes University invites applications for a fully funded, 3-year, full-time PhD studentship focused on developing advanced AI methods for battery State of Health (SOH) estimation. This project addresses the critical need for accurate SOH prediction in electric vehicles and energy storage systems, where safety, performance, and longevity are paramount. Traditional models often struggle with accuracy, generalisability, and computational efficiency, especially under diverse operating conditions. The research will pioneer a novel AI-based framework, the Ring Probabilistic Logic Neural Network (RPLNN), which integrates probabilistic logic with neural computation. The RPLNN aims to enhance the robustness and interpretability of SOH predictions. Unlike conventional deep learning models, the RPLNN constrains information flow through a ring-based structure governed by probabilistic logic rules, improving interpretability, data efficiency, and resistance to data drift. The model will be developed, trained, and experimentally validated using lithium-ion cell data provided by Jaguar Land Rover (JLR), offering a unique opportunity to work with real-world industry data. Supervision and Research Environment: The project will be supervised by Professor Shahab Resalati (Director of Studies) and Dr Aydin Azizi, both of whom have expertise in AI, battery systems, and engineering applications. Oxford Brookes University provides a vibrant research environment with access to state-of-the-art facilities and strong industry links. Funding: The studentship offers a bursary of £20,780 per annum for three years. University fees at the home rate are covered. International and EU students without Settled Status must pay the difference between home and international fees. Visa and associated costs are not included. Eligibility: Applicants should hold a first or upper second-class honours degree from a UK Higher Education Institution or an equivalent qualification. International/EU applicants must provide a valid IELTS Academic test certificate (or equivalent) with an overall minimum score of 6.0 and no score below 5.5, issued within the last 2 years by an approved test centre. Application Process: Apply directly via the university portal. Required documents include a cover letter, CV, details of two referees (at least one academic), a research proposal, degree certificates and transcripts, a passport scan, and evidence of English language qualification (for international/EU candidates). The application deadline is 20th February 2026, with interviews to be confirmed (online). The successful candidate will start in September 2026. For further information or queries, contact Professor Shahab Resalati at [email protected] or the studentships office at [email protected] . For application details, visit the Oxford Brookes University application portal .

[£20,780 per annum bursary. University fees covered at the home rate; international/EU students without Settled Status must pay the difference. Bench fees included. Visa and associated costs not covered.] This fully funded PhD studentship at Oxford Brookes University offers an exciting opportunity to advance battery State of Health (SOH) estimation using cutting-edge artificial intelligence. The project, supervised by Professor Shahab Resalati and Dr Aydin Azizi, focuses on developing and experimentally validating a novel AI framework: the Ring Probabilistic Logic Neural Network (RPLNN). This approach fuses probabilistic logic with neural computation to improve the robustness, interpretability, and data efficiency of SOH predictions for lithium-ion batteries, which are critical in electric vehicles and energy storage systems. Current SOH models often struggle to balance accuracy, generalisability, and computational efficiency across diverse operating conditions. The RPLNN model addresses these challenges by constraining information flow through a ring-based structure governed by probabilistic logic rules, enhancing interpretability and resistance to data drift. The project will utilize lithium-ion cell data supplied by Jaguar Land Rover (JLR), providing a strong industrial link and real-world relevance. The studentship is open to home, EU, and international students. It provides a bursary of £20,780 per annum, covers university fees at the home rate, and includes bench fees. International and EU students without Settled Status must pay the difference between home and international fees, and visa costs are not covered. The position is full-time for three years, with a start date in September 2026. The application deadline is 20th February 2026, and interviews will be held online (date TBC). Applicants should hold a first or upper second-class honours degree from a UK Higher Education Institution or an equivalent qualification. International/EU candidates must provide a valid IELTS Academic test certificate (or equivalent) with an overall minimum score of 6.0 and no score below 5.5, issued within the last 2 years. The application should include a cover letter, CV, details of two referees (at least one academic), a research proposal, degree certificates and transcripts, a passport scan, and evidence of English language qualification if required. To apply, use the Oxford Brookes University portal via the provided link. For further information or queries, contact Professor Shahab Resalati at [email protected] or the studentships team at [email protected].

[Bursary of £20,780 per annum. University fees at the home rate are covered; international/EU students without Settled Status must pay the difference between home and international fees. Visas and associated costs are not covered.] This fully funded PhD studentship at Oxford Brookes University offers an exciting opportunity to advance battery state of health (SOH) estimation for electric vehicles using innovative AI techniques. The project, in collaboration with Jaguar Land Rover, focuses on developing Ring Probabilistic Logic Neural Networks (RPLNN) to improve interpretability, data efficiency, and resistance to drift in battery SOH estimation. Unlike traditional deep learning models, the RPLNN framework integrates neural computation with probabilistic logic rules in a ring-based structure, addressing critical limitations in current AI-based approaches. The studentship is open to home, EU, and international students. University fees at the home rate are covered, and a generous bursary of £20,780 per annum is provided. International and EU students without Settled Status must cover the difference between home and international fees, and visa costs are not included. The position is full-time for three years, with a start date in September 2026. The application deadline is 20 April 2026, and interviews will be held online. Applicants should hold a Master’s degree (or equivalent) in Electrical Engineering, Control Engineering, Mechatronics, or Robotics, with a strong background in dynamic system theory. Essential skills include applied intelligent control techniques, machine learning, artificial intelligence, control systems, system modelling, and data-driven approaches. Experience with MATLAB/Simulink and relevant toolboxes, as well as knowledge of battery systems and battery management systems, is required. Candidates should demonstrate strong analytical and problem-solving abilities, excellent English communication skills, and motivation for high-quality research leading to publications. Desirable qualifications include experience with machine learning or deep learning models, state estimation techniques (such as Kalman filters), prior battery modelling or testing experience, familiarity with electric vehicles, energy storage systems, or smart energy technologies, and evidence of research activity. International/EU applicants must provide a valid IELTS Academic test certificate (minimum overall score 6.0, no score below 5.5, issued within the last 2 years). The project aims to address the challenge of balancing accuracy and computational efficiency in battery SOH estimation, which is crucial for electric vehicle safety and longevity. The RPLNN approach promises enhanced interpretability and robustness, making it a valuable contribution to the field of intelligent battery management. To apply, candidates should contact [email protected] before submitting their application. Applications must be made directly via the university portal and should include a cover letter, CV, details of two referees (at least one academic), degree certificates and transcripts, a scan of the passport, and evidence of valid English language qualification if required. For further queries, applicants can contact [email protected].