Explore a range of fully funded and competition-funded PhD opportunities at the University of Manchester for the 2025-2026 academic year. These projects span diverse fields, from materials science and engineering to social sciences, offering cutting-edge research opportunities with access to world-class facilities and expert supervision.
Note whether each position is fully funded, competition-funded (potentially self-funded), or restricted to specific countries. You can also checkout 24 Fully Funded PhD Opportunities at Aarhus University, Denmark
All Available Positions
Digital Twins of Microstructure: Predictive Modelling of Fatigue in Turbine Wheels
Project Details
This fully funded PhD project at the University of Manchester, in collaboration with Cummins, focuses on developing microstructure-aware simulation models to predict fatigue and damage in turbine wheels. You’ll work at the forefront of materials science and solid mechanics, using real-world microstructure data to simulate thermo-mechanical behavior at the grain scale. The research aims to enhance the performance and reliability of high-performance components for automotive and energy applications, with potential connections to hydrogen engine research and digital twin technologies.
You will:
- Develop physics-based microstructural models to simulate damage and fatigue.
- Use experimental data (grain orientation, defect density, residual stress) to inform and validate models.
- Identify key microstructural features that influence fatigue life.
- Propose optimization strategies for turbine wheel design.
- Collaborate with an experimental PhD student and industrial researchers.
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You’ll gain skills in:
- Computational modeling (e.g., FEM, crystal plasticity, phase-field methods).
- Multiscale mechanics and microstructure-property relationships.
- Python/C++/Matlab-based simulation and data analysis.
- Industry-facing research and technology transfer.
This project offers access to world-class experimental and computational facilities and a cross-institutional, interdisciplinary research environment. This project is fully funded for both UK and international students.
Eligibility
- A First or strong Upper Second-Class degree in Mechanical Engineering, Materials Science, Physics, or a related discipline.
- Essential background in computational mechanics, materials modeling, or engineering mathematics.
- Desirable: Prior experience with simulation tools or microstructural modeling.
- Open to both UK and international students.
Apply for This Project
To apply, submit your application through the University of Manchester’s online portal: Apply here. Contact the supervisor, Prof. Andrey Jivkov, before applying to discuss your academic background and motivation.
Application Deadline: 30 July 2025
Carbon Footprint Optimisation for Power Transformers
Project Details
This industry-funded PhD project at the University of Manchester aims to reduce the carbon footprint of power transformers through a comprehensive life cycle assessment (LCA). With the global push for carbon net-zero by 2050, this research will focus on optimizing the design, manufacture, operation, and disposal of power transformers to minimize their environmental impact while maintaining reliability and cost-effectiveness.
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You will:
- Develop a multi-objective optimization method integrating carbon footprint, total cost of ownership (TCO), and transformer reliability.
- Assess the entire transformer life cycle, including raw materials, manufacturing, transport, operation, maintenance, disposal, and recycling.
- Collaborate with transformer users, manufacturers, and material providers to refine optimization methodologies.
- Investigate the impact of design modifications on transformer reliability.
- Contribute to sustainable engineering solutions for a critical component in power systems.
You’ll gain expertise in:
- Life cycle assessment and carbon footprint analysis.
- Multi-objective optimization techniques.
- Transformer design and reliability analysis.
- Industry collaboration and applied engineering research.
This project offers a unique opportunity to address global sustainability challenges in the energy sector while working with cutting-edge industry partners. This project is fully funded for both UK and international students.
Eligibility
- A First-Class honors degree or Master’s (or international equivalent) in a relevant science or engineering discipline.
- Demonstrated research experience through contributions to international journal or conference publications.
- Open to both UK and international students.
Apply for This Project
To apply, submit your application through the University of Manchester’s online portal: Apply here. Before applying, send your CV and detailed transcripts to Prof. Zhongdong Wang ([email protected]) and Dr. Shanika Matharage ([email protected]).
Application Deadline: 15 August 2025
Knowledge Graph for Large Language Model Explainability and Evaluation
Project Details
This PhD project at the University of Manchester’s Department of Computer Science explores the use of Knowledge Graphs to enhance the explainability and evaluation of Large Language Models (LLMs) like GPT and Llama. LLMs face challenges such as hallucination, limited domain knowledge, and insufficient reasoning capabilities. This research aims to address these issues by leveraging Knowledge Graphs—multi-relational graphs like Wikidata and ontologies like SNOMED CT—for evaluating LLMs and enabling explainable AI in applications like question answering and fact verification.
You will:
- Develop methods to evaluate LLMs’ domain knowledge, reasoning capabilities, and explainability using Knowledge Graphs.
- Create benchmarks from large-scale or logic-equipped Knowledge Graphs for complex evaluation tasks.
- Design experiments to assess specific LLM capabilities quantitatively.
- Retrieve relevant knowledge and infer justifications to improve LLM explainability.
- Align LLMs with logical rules to enhance their reasoning abilities.
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You’ll gain expertise in:
- Neural-symbolic AI combining Machine Learning, NLP, and Symbolic AI.
- Knowledge Representation and Reasoning.
- Semantic technologies and Knowledge Graph applications.
- Advanced programming for complex systems.
This project offers a unique opportunity to advance trustworthy and explainable AI systems, with applications in high-safety domains, within the Information Management Group’s interdisciplinary research environment. This project is competition-funded and may require self-funding.
Eligibility
- An excellent undergraduate degree in Computer Science or Mathematics (or related discipline); a relevant Master’s degree is preferred.
- Confidence and independence in programming complex systems in Java or Python.
- Previous academic or industry experience in Machine Learning, NLP, Knowledge Representation and Reasoning, or Semantic Technology.
- A First-Class or high Upper Second-Class (2:1) honors degree (or international equivalent), or an Upper Second-Class (2:1) honors degree with a Master’s degree at merit in a relevant discipline.
- English language qualifications (if applicable), as per University of Manchester PGR guidelines.
- Open to both UK and international students.
Apply for This Project
To apply, submit your application through the University of Manchester’s online portal: Apply here. Contact Dr. Jiaoyan Chen ([email protected]) or Prof. Uli Sattler ([email protected]) before applying to discuss your academic background and motivation.
Application Deadline: Applications accepted all year round
Nanomanufacturing of Large-Area Electronics for the Internet of Things
Project Details
This PhD project at the University of Manchester’s Department of Electronic and Electrical Engineering focuses on advancing flexible, large-area electronics for the Internet of Things (IoT) using environmentally friendly metal oxide semiconductors. The research aims to overcome a key challenge: the lack of high-performance hole-transporting (p-type) oxide semiconductors, enabling industry-viable complementary logic for next-generation electronics.
You will:
- Develop p-type oxide semiconductors to match electron-transporting counterparts.
- Work on nanomanufacturing techniques for flexible electronics.
- Collaborate with experts in large-area electronics at the Henry Royce Institute.
- Contribute to sustainable semiconductor technologies for IoT.
You’ll gain expertise in:
- Nanomanufacturing and semiconductor material development.
- Metal oxide electronics and complementary logic design.
- Advanced characterization and fabrication techniques.
- Industry-relevant research for sustainable electronics.
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This project is fully funded and offers a unique opportunity to drive innovation in eco-friendly electronics. This project is fully funded for both UK and international students.
Eligibility
- At least an Upper Second-Class (2:1) honors degree or a Master’s (or international equivalent) in a relevant science or engineering discipline.
- Open to both UK and international students.
Apply for This Project
To apply, submit your application through the University of Manchester’s online portal: Apply here. Contact Dr. Thomas Anthopoulos before applying to discuss your academic background and motivation.
Application Deadline: 18 November 2025
Speciation and Fate of Long-Lived, Redox Active Radionuclides in ‘Red Bed’ Mudstones
Project Details
This PhD project at the University of Manchester’s Department of Earth and Environmental Sciences investigates the behavior of long-lived, redox-active radionuclides (e.g., uranium, neptunium, selenium, technetium) in ‘Red Bed’ Mudstones, a potential host rock for the UK’s Geological Disposal Facility (GDF). The research aims to understand how iron-bearing minerals influence radionuclide mobility, providing critical data for radioactive waste disposal safety.
You will:
- Characterize iron-bearing phases in RBMs affecting redox and radionuclide sequestration.
- Study radionuclide reactions under relevant groundwater and geomicrobiological conditions.
- Conduct flow-through experiments using bulk and micro-focus imaging techniques.
- Use advanced techniques (XRD, µ-XRF, XAS, S(T)EM, ICP-MS, PHREEQC modeling).
- Collaborate with industrial and international partners via NWS and SATURN CDT.
You’ll gain expertise in:
- Environmental biogeochemistry and mineralogy.
- Advanced liquid and solid chemistry techniques.
- Radionuclide behavior in complex environments.
- Geochemical modeling and microbial profiling.
This project, conducted in NNUF RADER laboratories, is fully funded and offers networking through conferences and SATURN CDT training. This project is fully funded for UK students and international students (fee waivers may be available for those not requiring ATAS clearance).
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Eligibility
- At least an Upper Second-Class (2:1) honors degree or a Master’s (or international equivalent) in a relevant science or engineering discipline.
- Open to UK students and international students (fee waivers may be available for those not requiring ATAS clearance).
- Applicants from the EU, US, and Australia are encouraged to apply.
Apply for This Project
To apply, complete the enquiry form on the SATURN CDT website: Apply here. Contact Prof. Katherine Morris ([email protected]) and Dr. Lucie Stetten ([email protected]) after submitting to discuss your suitability.
Application Deadline: Applications accepted all year round
Joint Modeling of Longitudinal and Time-to-Event Data
Project Details
This PhD project at the University of Manchester’s Department of Mathematics focuses on joint modeling of longitudinal and time-to-event data, analyzing correlations between repeated measurements (e.g., blood sugar levels) and event timings (e.g., medical complications). The research aims to improve prediction accuracy for applications in clinical studies, such as diabetes management.
You will:
- Develop statistical models for longitudinal and time-to-event data.
- Investigate correlations between repeated measurements and event timing.
- Apply joint modeling to real-world datasets.
- Enhance risk and progression estimation in health contexts.
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You’ll gain expertise in:
- Advanced statistical modeling and biostatistics.
- Longitudinal and survival data analysis.
- Statistical software (e.g., R, Python).
- Real-world health data applications.
This project is fully funded and offers a chance to advance statistical research with practical impact. This project is fully funded for both UK and international students.
Eligibility
- A Master’s degree (or international equivalent) in Statistics, Data Science, or Biostatistics.
- Open to both UK and international students.
Apply for This Project
To apply, submit your application through the University of Manchester’s online portal: Apply here. Contact Dr. Taban Baghfalaki before applying to discuss your academic background and motivation.
Application Deadline: Applications accepted all year round
Enabling High-Accuracy First Principles Modelling of Nanoscale Systems: Many-Body Quantum Embedding in Linear-Scaling DFT
Project Details
This PhD project at the University of Manchester’s Department of Materials aims to enhance density functional theory (DFT) for nanoscale systems by integrating quantum embedding with linear-scaling DFT. The research addresses DFT limitations (e.g., band gap underestimation) using advanced methods like GW approximation and Bethe-Salpeter equation, with applications in quantum technology, photovoltaics, and biochemistry.
You will:
- Develop quantum embedding methods for GW and BSE within linear-scaling DFT.
- Implement techniques in the ONETEP code for excited state calculations.
- Simulate systems like defects for quantum technology or organic photovoltaics.
- Optionally explore wavefunction-based methods.
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You’ll gain expertise in:
- Computational modeling of electronic structures.
- Linear-scaling DFT and quantum embedding.
- Code development (Python, FORTRAN beneficial).
- Applications in quantum technology and photovoltaics.
This project offers a chance to advance computational materials science. This project is competition-funded and may require self-funding.
Eligibility
- At least an Upper Second-Class (2:1) honors degree or a Master’s (or international equivalent) in Physics, Chemistry, Materials Science, or a related discipline.
- Interest in computational modeling and code development.
- Coding experience (Python, FORTRAN) beneficial but not required.
- Open to both UK and international students.
Apply for This Project
To apply, submit your application through the University of Manchester’s online portal: Apply here. Contact Dr. Joseph Prentice before applying to discuss your academic background and motivation.
Application Deadline: Applications accepted all year round
Understanding the Influence of Extended Defects and Surfaces on Defects for Quantum Technology Applications with First Principles Modelling
Project Details
This PhD project at the University of Manchester’s Department of Materials uses first-principles modeling to study how extended defects (e.g., dislocations) and surface effects impact light-matter interactions in quantum technology materials, such as nitrogen-vacancy centers in diamond. The research employs advanced methods like linear-scaling time-dependent DFT (LS-TDDFT) and quantum embedding to simulate large-scale systems.
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You will:
- Simulate defect and surface effects on quantum material properties.
- Use LS-TDDFT, quantum embedding, and machine learning potentials.
- Compare results with experimental data and collaborate with leading groups.
- Explore materials for quantum computing, sensing, and communications.
- Optionally develop advanced computational modeling methods.
You’ll gain expertise in:
- First-principles modeling of materials.
- Advanced computational techniques (LS-TDDFT, quantum embedding).
- Light-matter interactions in quantum materials.
- Experimental collaboration.
This project advances quantum technology materials through computational modeling. This project is competition-funded and may require self-funding.
Eligibility
- At least an Upper Second-Class (2:1) honors degree or a Master’s (or international equivalent) in Physics, Chemistry, Materials Science, or a related discipline.
- Interest in computational modeling and/or quantum technology materials.
- Interest in code development beneficial but not required.
- Open to both UK and international students.
Apply for This Project
To apply, submit your application through the University of Manchester’s online portal: Apply here. Contact Dr. Joseph Prentice before applying to discuss your academic background and motivation.
Application Deadline: Applications accepted all year round
Deep Learning-Based Mechanistic Elucidation of Processes Mediated by Heterogeneous Catalysts
Project Details
This PhD project at the University of Manchester’s Department of Chemistry develops machine learning tools to analyze kinetic data and predict reaction mechanisms for heterogeneous catalytic processes, such as Fischer-Tropsch and methanol synthesis. In collaboration with BP, the research aims to enhance catalyst design by understanding activation and deactivation pathways.
You will:
- Develop machine learning tools for kinetic data analysis in heterogeneous catalysis.
- Predict reaction mechanisms and deactivation pathways.
- Design experiments to maximize mechanistic insights.
- Apply models to industrially relevant processes.
You’ll gain expertise in:
- Machine learning for chemical applications.
- Heterogeneous catalysis and reaction mechanisms.
- Kinetic data processing and experimental design.
- Industry-relevant catalysis research.
This project is fully funded by an EPSRC Industrial Doctorate Landscape Award with BP. This project is fully funded for both UK and international students.
Eligibility
- At least an Upper Second-Class (2:1) honors degree or a Master’s (or international equivalent) in Chemistry, Chemical Engineering, or a related discipline.
- Open to both UK and international students.
Apply for This Project
To apply, submit your application through the University of Manchester’s online portal: Apply here. Contact Prof. I. Larrosa before applying to discuss your academic background and motivation.
Application Deadline: Applications accepted all year round
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Macaronesian Climate Change, Early Human Occupation, and Erosion
Project Details
This PhD project at the University of Manchester’s Department of Earth and Environmental Sciences explores how climate change and early human occupation impacted erosion and geological evolution in the Macaronesian islands (Terceira, Faial, Madeira). Using sediment cores, the research investigates climatic shifts, deforestation, and erosion rates, with implications for future environmental changes.
You will:
- Analyze sediment cores using MSCL, XRF, CT scanning, grain size, and elemental analysis.
- Propose new radiocarbon dates to the NERC 14C facility.
- Conduct lipid biomarker analysis to assess climatic and human impacts.
- Study terrigenous particle deposition and sea-level effects on erosion.
You’ll gain expertise in:
- Sedimentary geology and environmental geochemistry.
- Core analysis techniques (MSCL, XRF, CT scanning).
- Lipid biomarker analysis and radiocarbon dating.
- Igneous petrology and environmental science.
This project includes training in Lisbon and offers career opportunities in environmental science. This project is competition-funded and may require self-funding.
Eligibility
- At least an Upper Second-Class (2:1) honors degree or a Master’s (or international equivalent) in Sedimentary Geology, Environmental Science, or a related discipline.
- Background in sedimentary geology or environmental science preferred.
- Open to both UK and international students.
Apply for This Project
To apply, submit your application through the University of Manchester’s online portal: Apply here. Contact Dr. N. Mitchell, Dr. B. Van Dongen, Dr. M. Hartley, or Dr. Rui Quartau before applying to discuss your academic background and motivation. For faculty funding (President’s/Dean’s Doctoral Scholarship), ask your supervisor to nominate you after applying.
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Application Deadline: Applications accepted all year round (faculty funding deadlines: Round 1 – 20 December 2025, Round 2 – 28 February 2026)
Welded Connection of Aluminium Alloys for Construction Industry
Project Details
This PhD project at the University of Manchester’s Department of Mechanical, Aerospace and Civil Engineering investigates strength loss in welded aluminium alloys for construction. The research focuses on microstructural analysis of the heat-affected zone, material property degradation, and the impact on load-carrying capacity and failure modes.
You will:
- Conduct microstructural analysis of the heat-affected zone in welded aluminium alloys.
- Investigate strength reduction near welds.
- Evaluate structural effects on load-carrying capacity and failure modes.
- Explore welding technologies and their impact.
You’ll gain expertise in:
- Microstructural analysis of metallic materials.
- Welding technologies and structural implications.
- Material property testing.
- Structural engineering for construction.
This project advances aluminium alloy applications in construction. This project is competition-funded and may require self-funding.
Eligibility
- At least an Upper Second-Class (2:1) honors degree or a Master’s (or international equivalent) in Mechanical Engineering, Materials Science, Civil Engineering, or a related discipline.
- Background in metallic materials or welding technologies preferred.
- Open to both UK and international students.
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Apply for This Project
To apply, submit your application through the University of Manchester’s online portal: Apply here. Contact Dr. M. Su before applying to discuss your academic background and motivation.
Application Deadline: Applications accepted all year round
Machine Learning with Bio-Inspired Neural Networks
Project Details
This PhD project at the University of Manchester’s Department of Computer Science explores machine learning with biologically inspired spiking neural networks (SNNs). SNNs mimic the brain’s low-power processing, offering energy-efficient AI for neuromorphic hardware. The research combines biological learning principles with deep learning to develop advanced algorithms.
You will:
- Investigate bio-inspired learning for SNNs.
- Develop algorithms for image classification, segmentation, object detection, and NLP.
- Explore online learning, spatial/temporal credit assignment, and in-sensor computing.
- Use simulation tools (PyTorch, TensorFlow, SNN tools) and HPC/GPUs/neuromorphic hardware.
You’ll gain expertise in:
- Bio-inspired AI and spiking neural networks.
- Machine learning and deep learning.
- Neuromorphic computing.
- Advanced computational modeling.
This project advances energy-efficient AI technologies. This project is competition-funded and may require self-funding.
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Eligibility
- At least an Upper Second-Class (2:1) honors degree or a Master’s (or international equivalent) in Computer Science, Engineering, or a related discipline.
- Open to both UK and international students.
Apply for This Project
To apply, submit your application through the University of Manchester’s online portal: Apply here. Contact Dr. Oliver Rhodes ([email protected]) before applying to discuss your academic background and motivation.
Application Deadline: Applications accepted all year round
Understanding Sustainable H2 Production via Nonthermal Plasma Catalysis Using Advanced Spectroscopy Techniques
Project Details
This PhD project at the University of Manchester’s Department of Materials develops sustainable hydrogen (H₂) production using nonthermal plasma catalysis under mild conditions. The research designs porous catalysts and uses advanced spectroscopy to optimize energy efficiency and understand catalytic mechanisms, supporting the UK’s Net Zero strategy.
You will:
- Develop porous catalysts (zeolites, MOFs, metal oxides) for nonthermal plasma catalysis.
- Use spectroscopy (DRIFTS, XAS/XAFS, XRD) at UoM and Diamond Light Source.
- Compare thermal and nonthermal plasma system performance.
- Optimize reaction conditions for H₂ production.
You’ll gain expertise in:
- Catalyst synthesis and characterization.
- Nonthermal plasma catalysis.
- Advanced spectroscopic methods.
- Sustainable energy solutions.
This fully funded project offers access to world-class facilities and conference opportunities. This project is fully funded for UK students; EU/international students may apply (eligibility determined case-by-case).
Eligibility
- A First or Upper Second-Class honors degree (or international equivalent) in Chemistry, Chemical Engineering, Materials, or a related discipline.
- Interest in catalysis and/or advanced characterization methods.
- Open to UK students; EU students with settled/pre-settled status and international students may apply (eligibility determined case-by-case).
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Apply for This Project
To apply, submit your application through the University of Manchester’s online portal: Apply here. Contact Dr. Shanshan Xu ([email protected]) before applying to discuss your academic background and motivation.
Application Deadline: Applications accepted all year round
Organic Coatings Microstructure: Insights from Molecular Simulations
Project Details
This PhD project at the University of Manchester’s Department of Chemical Engineering investigates the microstructure of organic coatings (e.g., epoxy, phenolic resins) for corrosion protection using molecular dynamics and machine learning. The research aims to understand polymer-substrate interfaces to improve coating formulations for industries like construction and aerospace.
You will:
- Develop a workflow to assess epoxy and phenolic resin interfaces.
- Use molecular dynamics to model polymer crosslinking near surfaces.
- Apply machine learning to identify features linked to coating performance.
- Investigate precursors and additives for enhanced coatings.
You’ll gain expertise in:
- Molecular dynamics simulations.
- Machine learning for material analysis.
- Interface and microstructure analysis.
- Corrosion protection technologies.
This project advances corrosion-resistant coatings. This project is competition-funded and may require self-funding.
Eligibility
- A First or Upper Second-Class (2:1) honors degree or a Master’s (or international equivalent) in Chemical Engineering, Chemistry, Physics, Applied Mathematics, or Materials Science.
- Knowledge of programming is essential.
- Familiarity with molecular simulations beneficial.
- Open to both UK and international students.
Apply for This Project
To apply, submit your application through the University of Manchester’s online portal: Apply here. Contact Dr. Flor Siperstein ([email protected]) before applying to discuss your academic background and motivation.
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Application Deadline: Applications accepted all year round
Computational Discovery of MOFs for Energy Applications Using ML-Assisted Inverse Design Strategies
Project Details
This PhD project at the University of Manchester’s Department of Chemical Engineering discovers new Metal-Organic Frameworks (MOFs) for hydrogen energy applications using molecular simulations and machine learning-assisted inverse design. In collaboration with Oak Ridge National Laboratory and IBM, the research aims to design MOFs for hydrogen production, storage, and utilization, supporting the UK’s net-zero goals.
You will:
- Use molecular simulations and machine learning for MOF design.
- Develop inverse design strategies for high-performance MOFs.
- Collaborate with leading institutions.
- Contribute to MOF synthesis and testing.
You’ll gain expertise in:
- Computational materials design.
- Machine learning for inverse design.
- MOF chemistry and hydrogen energy.
- Interdisciplinary research collaboration.
This project is fully funded by the Strategic Doctoral Landscape Award. This project is fully funded for UK students and those with settled status.
Eligibility
- At least an Upper Second-Class (2:1) honors degree or a Master’s (or international equivalent) in Chemical Engineering, Materials Science, or a related discipline.
- Open to UK students and those with settled status.
Apply for This Project
To apply, submit your application through the University of Manchester’s online portal: Apply here. Contact Prof. Lev Sarkisov before applying to discuss your academic background and motivation.
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Application Deadline: 01 August 2025
Life Cycle Assessment Applied to Chemicals Production
Project Details
This PhD project, part of the BBSRC-funded SuCCEED Prosperity Partnership with Shell, focuses on sustainable chemical production using life cycle assessment (LCA). The research develops a framework to evaluate environmental impacts of fossil-derived and bio-based commodity chemicals, identifying sustainable bio-based alternatives for net-zero transitions.
You will:
- Develop an LCA framework for chemical production.
- Evaluate exemplar commodity chemicals and bio-based alternatives.
- Identify sustainable bio-based production pathways.
- Support net-zero chemical manufacturing.
You’ll gain expertise in:
- Life cycle assessment methodology.
- Environmental impact analysis.
- Industrial biotechnology.
- Sustainable chemical engineering.
This fully funded project offers collaboration with industry experts and BBSRC DTP training. This project is fully funded for UK students.
Eligibility
- At least an Upper Second-Class (2:1) honors degree or a Master’s (or international equivalent) in Chemical Engineering or a related engineering discipline.
- Open to UK students.
Apply for This Project
To apply, submit your application through the University of Manchester’s online portal: Apply here. Contact Dr. Rosa Cuellar-Franca or Dr. J. Winterburn before applying to discuss your academic background and motivation.
Application Deadline: 01 September 2025
Feminist Global Politics in the Age of Backlash
Project Details
This PhD project, a collaboration between the University of Manchester’s School of Social Sciences and ODI, explores feminist frameworks in global politics amidst anti-gender backlash. Focusing on Feminist Foreign Policy (FFP), the research examines how feminist principles shape policy processes and outcomes, and the impact of anti-feminist campaigns.
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You will:
- Design a project to explore feminist foreign policy approaches.
- Analyze policy processes and outcomes influenced by feminism.
- Investigate anti-gender backlash effects on gender justice.
- Collaborate with ODI for global impact.
You’ll gain expertise in:
- Feminist approaches in international studies and politics.
- Policy analysis in global contexts.
- Interdisciplinary research with non-academic partners.
- Gender dynamics in geopolitics.
This project is fully funded by the HSIF Studentship with ODI. This project is fully funded for both UK and international students.
Eligibility
- A Bachelor’s (Honours) degree at 2:1 or above (or international equivalent) in a cognate subject.
- A Master’s degree with an overall average of 65% or above, a minimum dissertation mark of 65%, and no mark below 55% (or international equivalent).
- Experience working collaboratively and knowledge of feminist approaches in international studies, sociology, politics, or geography.
- English language proficiency (e.g., IELTS 7.0 overall, 7.0 in writing, 6.5 in other sections; TOEFL iBT 100 overall, 25 in all sections; or equivalent).
- Open to both UK and international students.
Apply for This Project
To apply, submit your application for PhD Politics through the University of Manchester’s online portal: Apply here. Indicate the HSIF Studentship in Section 9 and the project title in Section 6. Contact Prof. Toni Haastrup ([email protected]) before applying to discuss your academic background and motivation.
Application Deadline: 15 August 2025
