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- Tasks: Join a cutting-edge project using ML and NLP to revolutionise materials science.
- Company: Durham University, a leader in multidisciplinary research.
- Benefits: Full-time position with opportunities for impactful research and career growth.
- Why this job: Make a real difference in scientific discovery while working with advanced technologies.
- Qualifications: Experience in machine learning, natural language processing, and Python programming.
- Other info: Collaborate with experts across chemistry, physics, and computer science.
The predicted salary is between 36000 - 60000 £ per year.
Organisation/Company DURHAM UNIVERSITY Research Field Computer science Researcher Profile Recognised Researcher (R2) Established Researcher (R3) Country United Kingdom Application Deadline 4 Nov 2025 – 00:00 (UTC) Type of Contract Other Job Status Full-time Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No
Offer Description
The Role
Applications are invited for a Research Associate in Machine Learning and Natural Language Processing for Materials Science.
The project focuses on building a database and on combining advanced ML and NLP approaches to accelerate discovery in Molecular Solid Solutions (MoSS). Data will be gather collectively during the course of the project. NLP techniques will be used to automatically extract key data points from published papers and reports, building and enriching a comprehensive MoSS database. Machine Learning will then be applied to this database, alongside quantum chemistry methods, to predict MoSS formation, model dopant effects on crystal structures, and identify optimal dopants for tuning host compounds. The project is highly multidisciplinary, linking computational and physical chemistry, physics, and computer science at Durham University and partner institutions.
The successful applicant will be expected to design and implement NLP pipelines for literature mining, and to select, adapt, and develop ML models, predictive models in PyTorch, and interpretable methods such as SHAP and causal inference. This dual approach will both expand the data resource and provide powerful predictive insights into structure-property relationships. The PDRA will also produce python code for advanced scientific data processing. This will then be implemented in the data pipelines of the project. The applicant will also serve as data champion to the project.
The postholder will be expected to contribute actively to high-quality research outputs, including publications, software tools, and follow-on grant proposals.
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PDRA in Machine Learning and Natural Language Processing for Materials Science employer: Durham University
Contact Detail:
Durham University Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land PDRA in Machine Learning and Natural Language Processing for Materials Science
✨Tip Number 1
Network like a pro! Reach out to current or former researchers at Durham University, especially those in Machine Learning and NLP. A friendly chat can give us insider info on the team and the project, plus it shows our genuine interest.
✨Tip Number 2
Prepare for the interview by brushing up on your ML and NLP skills. We should be ready to discuss how we’d tackle specific challenges in the project. Practising common interview questions can help us articulate our thoughts clearly.
✨Tip Number 3
Showcase our passion for materials science! When we talk about our previous projects or experiences, let’s highlight how they relate to the role. This will help us stand out as candidates who truly care about the field.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure our application gets noticed. Plus, we can keep track of our application status easily, which is always a bonus.
We think you need these skills to ace PDRA in Machine Learning and Natural Language Processing for Materials Science
Some tips for your application 🫡
Tailor Your CV: Make sure your CV is tailored to highlight your experience in Machine Learning and Natural Language Processing. We want to see how your skills align with the project, so don’t hold back on showcasing relevant projects or research!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you’re passionate about this role and how your background makes you a perfect fit. We love seeing enthusiasm and a clear understanding of the project’s goals.
Showcase Your Technical Skills: Since this role involves Python coding and ML model development, be sure to mention any specific tools or frameworks you’ve worked with, like PyTorch. We’re keen to know how you can contribute to our data pipelines!
Apply Through Our Website: Don’t forget to apply through our website! It’s the best way for us to keep track of your application and ensure it gets the attention it deserves. Plus, it’s super easy to do!
How to prepare for a job interview at Durham University
✨Know Your Stuff
Make sure you brush up on the latest trends in Machine Learning and Natural Language Processing, especially as they relate to materials science. Familiarise yourself with key concepts like NLP pipelines and predictive modelling in PyTorch. This will show that you're not just a candidate, but a passionate expert ready to contribute.
✨Showcase Your Projects
Prepare to discuss any relevant projects you've worked on, particularly those involving data processing or ML models. Be ready to explain your role, the challenges you faced, and how you overcame them. This gives the interviewers insight into your practical experience and problem-solving skills.
✨Ask Smart Questions
Come armed with thoughtful questions about the project and the team at Durham University. Inquire about their current methodologies or future directions for the MoSS database. This demonstrates your genuine interest in the role and helps you assess if it's the right fit for you.
✨Be a Team Player
Since this role involves collaboration across multiple disciplines, highlight your teamwork experiences. Share examples of how you've successfully worked with others in research settings, and emphasise your willingness to contribute to high-quality outputs and grant proposals.
