Rakesh Mishra

A fully funded EPSRC DTP PhD studentship is available at the University of Huddersfield’s School of Computing and Engineering, specifically within the Centre of Thermofluids, Energy Systems and High-Performance Computing. The project, supervised by Dr Hossein Fatahian and Professor Rakesh Mishra, focuses on advanced design strategies for next-generation offshore horizontal-axis wind turbines. Research will involve developing a hybrid CFD–AI–experimental framework to optimize the aerodynamic and aeroacoustic performance of wind turbine blades, integrating active/passive flow control techniques, surrogate modelling, AI-driven multi-objective optimization, wake structure characterization, and wind tunnel experiments. The opportunity is highly interdisciplinary, combining fluid dynamics, machine learning, and experimental methods, and aligns with the UK’s Net Zero and renewable energy targets. Applicants must be UK home students with a First Class or Master's degree in Mechanical Engineering, Aerospace Engineering, Renewable Energy, or related fields, and have strong skills in fluid mechanics, aerodynamics, and CFD tools. Desirable skills include machine learning, surrogate modelling, MATLAB/Python, and experimental fluid dynamics. The studentship covers tuition fees and a tax-free stipend starting at £20,780 for 2025/26, funded for 3 years via the EPSRC Doctoral Training Programme. The application deadline is August 1, 2025. Interested candidates should contact Dr Hossein Fatahian and apply via the University’s postgraduate portal, submitting the required documents and references.

University of Huddersfield School of Computing and Engineering is advertising a fully funded PhD studentship starting in October 2026 in the area of patient-specific digital twins, fluid dynamics, computational fluid dynamics (CFD), Large Eddy Simulation (LES), machine learning, aeroacoustics, and healthcare engineering . The featured project is “Patient-Specific Digital Twins for Optimising Airflow and Voice in Above-Cuff Vocalisation Using LES and Machine Learning.” It focuses on improving Above-Cuff Vocalisation (ACV) for tracheostomy patients by replacing trial-and-error clinical practice with predictive modelling. The project will use high-fidelity CFD/LES to simulate airflow and sound generation in anatomically realistic airways, then build machine-learning models to predict and optimise airflow and voice outcomes within a digital twin framework. Funding: tuition fees plus a tax-free stipend/bursary starting at £21,805 per year for 3 years . The opportunity is open to UK and international applicants . Eligibility highlights: a first-class or upper second-class honours degree (or equivalent) in Mechanical, Biomedical, Aerospace Engineering, or a closely related discipline. A relevant Master’s degree is desirable. Applicants should have a background in fluid mechanics and computational modelling; experience with CFD tools, numerical methods, and Python or MATLAB is advantageous. Interest in interdisciplinary research at the interface of engineering, AI, and healthcare is preferred. International applicants must meet the English language requirement noted on the scholarship page (IELTS 6.5 with no element below 6, or equivalent, unless recent UK study applies). Application window: full application deadline is 15 May 2026 . Shortlisted candidates are expected to be interviewed on 26–27 May 2026 , and the scholarship page notes that shortlisted candidates will be contacted by 20 May 2026 . Supervisory team: Dr Hossein Fatahian, Professor Rakesh Mishra, Professor Leigh Fleming (University of Huddersfield), and Dr Rasool Erfani (Manchester Metropolitan University). How to apply: email the full application to [email protected] with a motivational email naming the project, CV, transcripts/certificates, and proof of eligibility. Ask two referees to send references directly to the same email address.