Opportunities

2026 September entry📢

🚀 University of Manchester Fully-funded FSE Bicentenary PhD Opportunity – AI + 2D Materials Discovery 🚀 

We’re seeking ambitious, curious and highly motivated candidates to join an exciting PhD studentship in “AI-Powered Prediction of Raman Spectra and Structure Discovery for 2D Materials”, a game-changing project at the intersection of artificial intelligence, condensed matter physics, and materials science. https://www.findaphd.com/phds/project/fse-bicentenary-phd-ai-powered-prediction-of-raman-spectra-and-structure-discovery-for-2d-materials/?p188701

🔍 Why this project matters

• The vibrational fingerprint technique Raman spectroscopy is a cornerstone for characterising two-dimensional (2D) materials — it reveals layer number, stacking order, strain, disorder, doping, interlayer coupling and more. 

• Normally, interpreting Raman spectra means heavy first-principles calculations of phonons every time — expensive, slow and not scalable. This project aims to change the game by developing an AI model that maps from atomic structure → Raman spectra (forward map) and critically, the inverse map: from a target Raman response → propose 2D crystal structures. 

• The inverse map is especially thrilling: if you can say “I want a material that gives this Raman signature”, you’re really stepping into materials-by-design territory — unlocking control over electronic, optical and quantum properties.

• You’ll be working at the frontier of self-driving materials labs: unifying heterogeneous datasets, using machine learning to accelerate discovery, bridging experiment and computation. 

🎯 What we’re looking for

You’ll thrive here if you have:

• Strong background in physics/materials science/engineering or related discipline

• Good coding skills (Python, ML libraries) and enthusiasm for data-driven modelling

• Interest in condensed matter / 2D materials, spectroscopy (helpful but not essential)

• Self - motivated mindset: this is frontier research — you’ll help chart the course

• Ability to work across computation and experiment (or keen to engage with both)

🌟 What you’ll gain

• A fully-funded studentship (tuition + stipend) — enabling you to focus on big ideas, not funding worries

• Training in state-of-the-art AI for materials, 2D materials synthesis/characterisation, spectroscopy

• Opportunity to publish in high-impact journals, develop skills highly sought after in both academia and industry

• A chance to build something new: real open-ended discovery, not just incremental refinement

• Be part of a vibrant research group, working with internationally recognised researchers

📆 Deadline & how to apply

Applications are open now, with a deadline of 31 January 2026. 

To apply, please prepare: CV, cover letter (highlighting your interest and suitability), academic transcripts, and contact details for two referees.

Apply here: https://pgapplication.manchester.ac.uk/psc/apply/EMPLOYEE/SA/c/CIBAA_MNU.UMOAA_LOGIN_CMP.GBL?Page=CIBAA_LOGIN&Action=A

Commonwealth Split-site PhD Scholarships:

Applications are now open for our 2023 Commonwealth Split-site Scholarships. These scholarships are for PhD candidates from low and middle-income Commonwealth countries to spend 12 months at a UK university as part of their doctoral studies in their home country. 

More information here.

Please contact qian.yang@manchester.ac.uk or artem.mishchenko@gmail.com  

Application notes for PhD candidates:

We provide full funding (tuition fee waiver plus stipend) for outstanding candidates with proven abilities in research, English competence, and problem-solving abilities: make sure to reflect these in your CV.

Other opportunities: 

• Check out many of  the funding opportunities at the University of Manchester 

• PhDs in Science and Engineering 

• CDT in Science and Applications of Graphene and Related Nanomaterials (Graphene NOWNANO CDT).

• Dean's Doctoral Scholarship from the University of Manchester.

• Chinese Scholarship Council (CSC) PhD programme.

Early career reseachers can also join us through the following Schemes: