At a Glance
- Tasks: Explore machine learning methods for excited-state simulations in perovskite optoelectronics.
- Company: Join a dynamic research group at the University of Birmingham.
- Benefits: Fully funded 3.5-year PhD studentship with training in cutting-edge techniques.
- Other info: Collaborative environment with opportunities for professional growth and development.
- Why this job: Make a real impact on materials science and tackle complex challenges.
- Qualifications: Upper Second-Class Honours Degree in Physics, Materials Science, Chemistry, or related fields.
The predicted salary is between 18000 - 25000 £ per year.
Halide perovskites are a fascinating class of materials with a wide range of applications in (spin) optoelectronics. Their outstanding optoelectronic properties are strongly influenced by coupling between excited electronic states and the wildly vibrating atoms in these “soft semiconductors”. This can lead to the trapping of charge carriers and excitons (bound electron‑hole pairs), shifted and broadened spectral signatures, and – paradoxically – either constitute a desirable material property or limit device performance.
Understanding and predicting these excited‑state phenomena “from first principles” is essential for rational materials design, but the underlying numerical simulations are computationally expensive, limiting studies to small systems and short timescales. This project will explore how machine‑learning‑based methods can help overcome this bottleneck, opening the door to excited‑state simulations at scales and system sizes that are currently out of reach.
You will work at the interface of method development and materials physics, as part of a research group passionate about tackling complex materials challenges for materials with real‑world applications. The project offers training in first‑principles electronic‑structure and excited‑state methods (density functional theory and the GW and Bethe‑Salpeter‑Equation approaches), machine learning for atomistic simulation, and high‑performance computing, within an active, collaborative research group.
You will be based in the group of Prof. Linn Leppert in the School of Metallurgy and Materials at the University of Birmingham.
Background: We welcome applicants from Physics, Materials Science, Chemistry, or related disciplines who have at least an Upper Second‑Class Honours Degree or equivalent. A background including courses in quantum mechanics or quantum chemistry is required; prior experience with electronic‑structure theory or numerical simulation methods is an advantage but not essential, as full training will be provided.
Further details about the research group can be found at www.leppertlab.com.
Closing date: 31 August 2026.
Funding notes: This position is a fully funded 3.5-year PhD studentship (UK home fees only).
PhD Studentship: ML-Accelerated Excited-State Simulations of Perovskite Optoelectronics in Birmingham employer: University of Birmingham
The University of Birmingham is an exceptional employer, offering a dynamic work environment that fosters creativity and collaboration. With a strong commitment to employee development, staff have access to numerous growth opportunities and professional training, all while enjoying the benefits of a flexible working arrangement. Located in a vibrant city, the university promotes a culture of inclusivity and support, making it an ideal place for those seeking meaningful and rewarding employment.
StudySmarter Expert Advice🤫
We think this is how you could land PhD Studentship: ML-Accelerated Excited-State Simulations of Perovskite Optoelectronics in Birmingham
✨Join Data-Science Meetups
Get yourself along to local data-science meetups or workshops. They're goldmines for networking, and you'll learn from industry pros who might just point you in the direction of internships. Plus, discussing the latest trends with like-minded individuals can really amp up your game.
✨Utilise University Career Services
Check in with your uni's career services since they often have connections with companies looking for interns. They might even organise information sessions with firms, which can be a great chance for you to learn more about potential internships and make some key contacts.
✨Show Off Your Stuff on GitHub
If you're into data science, having a GitHub profile with your projects is essential. Make sure your portfolio is public and showcases your best work! Recruiters love to see your coding skills and problem-solving approach, and it’s a brilliant way to stand out.
✨Apply Directly on Our Website
Don’t forget to check out the internships listed on our site! It's always a good idea to apply directly through our website because it makes your application easier for our team to find, and you might just catch the hiring manager’s eye by showcasing exactly what you're passionate about in data science.
We think you need these skills to ace PhD Studentship: ML-Accelerated Excited-State Simulations of Perovskite Optoelectronics in Birmingham
Some tips for your application 🫡
Show Off Your Technical Skills:For a data science internship, we want to see those analytical skills shine! List your programming languages, like Python or R, and make sure to highlight any relevant projects or courses you've completed. If you've dabbled with tools like Pandas, NumPy, or machine learning algorithms, don’t hold back – include those in your CV!
Share Your Curiosity in Your Cover Letter:As an intern, your motivation and eagerness to learn are key! In your cover letter, talk about specific data science concepts that excite you and how this internship at University of Birmingham will help you grow. Share what you hope to achieve and how you plan to tackle real-world data problems - we love enthusiasm!
Include Any Relevant Certifications:If you've earned any certifications, such as from Coursera or DataCamp, make sure to include these in your application. They show us that you're proactive and committed to expanding your data science skillset. This could make a real difference in how we assess your application!
Keep It Relevant and Concise:Remember, as an intern, you don’t need to have decades of experience. Focus on showcasing relevant coursework, personal projects, or even related volunteer work in data science. Keep your CV and cover letter concise but impactful – we appreciate clear and straightforward communication!
How to prepare for a job interview at University of Birmingham
✨Brush Up on Your Coding Skills
As a data science intern, you might get grilled on your programming skills. Expect to tackle some coding challenges using languages like Python or R. We recommend practising basic algorithms or data manipulation tasks so you can show off your tech skills with confidence.
✨Show Off Your Projects
Prepare to discuss any projects you’ve done, whether in your studies or on your own time. Having a strong portfolio of data analyses or machine learning models will really set you apart. We can use platforms like GitHub to showcase your work to impress University of Birmingham.
✨Know Your Stats and ML Basics
Brush up on your statistics and machine learning concepts because interviewers love to dig into this! Be ready to explain your understanding of algorithms or how you would approach a given data problem. This will highlight your theoretical background alongside your practical skills.
✨Be Eager to Learn and Adapt
Internships are all about potential and growth. Make sure you convey your eagerness to learn and adapt to new tools or methodologies. Show University of Birmingham that you’re not just looking for experience, but that you're keen to contribute and grow within the team.
