Publisher
source

University of Nottingham

Just Landed

PhD Studentship in Sustainable Aviation Fuel Thermochemical Modelling University of Nottingham in United Kingdom

Degree Level

PhD

Field of study

Computer Science

Funding

The studentship covers tuition fees and provides a tax-free stipend of £24,000 per annum for four years. Funding is restricted to UK home-fee students.

Deadline

Jan 5, 2027

Country flag

Country

United Kingdom

University

University of Nottingham

Apply for this position

Keywords

Computer Science
Chemistry
Mechanical Engineering
Chemical Engineering
Aerospace Engineering
Heat Transfer
Thermophysics
ML

Suggested positions

Publisher
source

Imperial College London

Country flag

United Kingdom

PhD Scholarship in CFD, Machine Learning, and Shape Optimization for Flow Chemistry Applications at Imperial College London , Department of Chemical Engineering. This funded PhD project focuses on computational fluid dynamics (CFD) , machine learning , and shape optimization for flow chemistry applications. The research investigates impinging-jet mixers, a key technology in pharmaceutical manufacturing where mixing quality affects product consistency, crystallization, nanoparticle synthesis, and...

Computer Science
Chemistry
Mechanical Engineering
+6 more

1 month ago

Publisher
source

University of Southampton

Country flag

United Kingdom

PhD opportunity at the University of Southampton in circular lifecycle knowledge for aerospace titanium . This project sits at the intersection of Materials Science , Aerospace Engineering , Mechanical Engineering , and Computer Science . The research aims to create a traceable pathway from titanium scrap to requalified aerospace-grade feedstock using advanced powder metallurgy, materials passports, lifecycle data, AI/ML approaches, ontology design, and metadata schemas. You will work on titaniu...

Computer Science
Mechanical Engineering
Materials Science
+4 more

3 weeks ago

Publisher
source

University of Sheffield

Country flag

United Kingdom

Fully funded PhD opportunity at the University of Sheffield in Digital and Data-Driven Life Cycle Assessment for Low-Carbon Battery Manufacturing . The project sits within the EPSRC MADE4Manufacturing CDT and combines life cycle assessment , machine learning , digital technologies, and sustainable battery manufacturing . It offers strong industrial collaboration and training opportunities. Funding includes a £28,000 annual tax-free stipend and a £35,000 research and training support budget over ...

Computer Science
Environmental Science
Mechanical Engineering
+4 more

2 weeks ago

Publisher
source

University of Birmingham

Country flag

United Kingdom

The SurPhase Horizon Europe Marie Skłodowska-Curie Actions (MSCA) Doctoral Network is recruiting 12 fully funded PhD/Doctoral Candidate positions across the University of Birmingham in the United Kingdom, Sabancı University in Türkiye, the Norwegian University of Science and Technology (NTNU) in Norway, and the University of Padova in Italy. The doctoral projects focus on advanced surface engineering and thermal-fluid science, including flow boiling, condensation, additive manufacturing, Molecul...

Computer Science
Mechanical Engineering
Chemical Engineering
+5 more

5 days ago

Publisher
source

University of Southampton

Country flag

United Kingdom

University of Southampton is advertising a competition-funded PhD project titled “Genuinely high-order discretisation of Navier-Stokes equations using machine learning and its application to turbomachinery aeroacoustics.” The project sits in the Faculty of Engineering and Physical Sciences and focuses on computational fluid dynamics , Navier-Stokes equations , high-order discretisation , machine learning , neural networks , and turbomachinery aeroacoustics . The research aims to build a more sta...

Computer Science
Mechanical Engineering
Aerospace Engineering
+5 more

1 week ago

About this position

The University of Nottingham’s Faculty of Engineering is offering a PhD studentship on sustainable aviation fuel thermochemical modelling, conducted in partnership with Airbus. The project addresses challenges affecting the adoption of sustainable aviation fuels by examining their thermophysical and thermochemical behaviour under conditions typical of aircraft fuel systems.

The research will investigate how heat transfer, fuel chemistry, and physical behaviour interact. You will use computational, analytical, and machine learning approaches to model heat transfer to fuels, chemical changes, and changes in physical properties. Where appropriate, experimental work may be undertaken to generate validation data, although industrial and openly available literature will be used wherever possible.

The project offers interdisciplinary experience spanning chemical engineering, mechanical engineering, aerospace engineering, computational fluid dynamics, computational heat transfer, chemistry, and machine learning. The research is intended to support the transition to net-zero flights, with possible involvement in practical fuel-system design and testing.

You will join the Mechanical and Aerospace Systems research group within the Faculty of Engineering. The group brings together around 50 researchers, technicians, support staff, and academics and provides a supportive research environment with seminars, writing activities, social events, and a research office hub containing quiet workspaces and meeting areas.

Part of the PhD is expected to be based at the Airbus site in Bristol, alongside supervision support and training from both the University of Nottingham and Airbus.

Funding details

The studentship covers tuition fees and provides a tax-free stipend of £24,000 per annum for four years. Funding is restricted to UK home-fee students.

What's required

Applicants should be enthusiastic, self-motivated, rigorous researchers who work well in a team. They should have or be expected to gain a high 2:1 degree, preferably a first-class honours degree, in chemical, mechanical or aerospace engineering, chemistry, or a computer-science-related discipline. Good knowledge or experience in heat transfer is essential. Prior experience in computational fluid dynamics, chemistry, machine learning, or computational heat transfer is advantageous. Due to funding restrictions, the studentship is available only to UK citizens eligible for home fees.

How to apply

Email a CV, cover letter, and academic transcripts to Hadrian Moran through the Apply button. Suitable applicants will be interviewed and successful candidates will then be invited to make a formal application. Informal enquiries may be sent to Professor Carol Eastwick.

ApplyKite TwinLearns as you apply

Make your next move.

Prepare your application

Review drafts and confirm requirements with the university.