At a Glance
- Tasks: Conduct innovative research on drug crystallisation using advanced techniques like Raman spectroscopy and X-ray scattering.
- Company: Join a leading research team at the University of Leeds with a focus on interdisciplinary science.
- Benefits: Receive a tax-free maintenance grant of £19,237 per year for 3.5 years plus tuition fees covered.
- Other info: Collaborate with top researchers and access state-of-the-art facilities at Diamond Light Source.
- Why this job: Make a real impact in drug design while developing cutting-edge skills in machine learning and data analysis.
- Qualifications: Must have a First or Upper Second Class UK Bachelor’s degree in relevant fields.
The predicted salary is between 19237 - 19237 £ per year.
Eligibility: UK Applicants only
Funding: EPSRC Doctoral Training Partnership Studentship offering the award of fees, together with a tax-free maintenance grant of £19,237 per year for 3.5 years.
Lead Supervisor’s full name & email address: Dr Anuradha Pallipurath - a.r.pallipurath@leeds.ac.uk
Co-supervisor name: To be confirmed
Project summary: This interdisciplinary project presents an exciting opportunity for an ambitious scientist or engineer to work across the boundaries of chemistry, physics and engineering, with opportunities to develop a broad portfolio of skills. A combination of Raman spectroscopy and total X-ray scattering techniques will be used to study the crystallisation of drug molecules and molecular analogues, to determine the influence of functional groups on their crystallisation behaviour. Predictive control of industrial crystallisation requires an understanding of how drugs behave in solution, and getting experimental structural information in the solution state has not been possible until recently. With advances in computing facilities and in X-ray total scattering and Raman instrumentation, we can now realistically hope to establish the details of the intermolecular interactions between the drug and the surrounding chemical environment and the structural dynamics during crystallisation. The information gained in this project will enable improvements in process control and predictive modelling. The project will combine experimental work with researchers at Leeds and at the UK's national synchrotron radiation facility, Diamond Light Source, and will involve some development of computational data analysis code and molecular modelling. You will also have an opportunity to learn machine learning methods for the analysis of structural information together with the development of correlative analysis techniques. You will be funded by the Royal Society and EPSRC DTP.
Control of crystallisation requires the understanding of structural dynamics of the molecules in the phase from which they form. Most industrial methods involve the use of solvents to control crystallisation. While there are methods to predict how molecular interactions affect batch processing, they are limited in how well the solvent system can be represented through known chemical and physical parameters of individual components.
Synchrotron science has progressed in leaps and bounds recently and this allows for X-ray total scattering studies from non-crystalline materials such as solution phases as well as amorphous states. Further, Raman spectroscopy will provide information about molecular conformations for large molecules with flexible bonds. Together, these allow for more accurate molecular models to be generated and refined against experimental data. The resulting understanding of inter-molecular interactions will provide a wealth of new information, which can be used to improve predictive design and control of crystallisation using machine-learning methods.
This studentship will entail the development of correlative techniques using X-ray pair distribution function analysis and Raman spectroscopy together with molecular modelling of the experimental data. There will also be opportunities to explore the use of machine learning to mine the wealth of information generated from these techniques for the various systems studied.
Entry requirements: First or Upper Second Class UK Bachelor (Honours) or equivalent
Subject Area: Analytical Chemistry, Chemical Engineering, Materials Science, Applied Physics
Keywords: X-ray Scattering, Crystallisation, Machine Learning, Raman Spectroscopy
PhD Studentship: Predictive Design of Drug Co-Crystals Through Correlative Studies of Inter-Mol[...] in Leeds employer: University of Leeds
As a leading institution in scientific research, this PhD studentship offers an exceptional opportunity for UK applicants to engage in cutting-edge interdisciplinary work at the intersection of chemistry, physics, and engineering. With access to state-of-the-art facilities like the Diamond Light Source and a supportive environment that fosters innovation and collaboration, students will benefit from a generous tax-free maintenance grant and the chance to develop valuable skills in machine learning and data analysis, paving the way for meaningful career advancement in the field.
StudySmarter Expert Advice🤫
We think this is how you could land PhD Studentship: Predictive Design of Drug Co-Crystals Through Correlative Studies of Inter-Mol[...] in Leeds
✨Tip Number 1
Network like a pro! Reach out to your connections in the field of chemistry, physics, and engineering. Attend relevant events or webinars where you can meet potential supervisors or collaborators. Remember, sometimes it’s not just what you know, but who you know!
✨Tip Number 2
Prepare for interviews by brushing up on your technical knowledge. Be ready to discuss your understanding of crystallisation, machine learning, and spectroscopy techniques. We want to see your passion and expertise shine through!
✨Tip Number 3
Showcase your skills! Create a portfolio that highlights any relevant projects or research you've done. This could include presentations, papers, or even code you've developed. It’s a great way to demonstrate your capabilities beyond just your CV.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets the attention it deserves. Plus, we love seeing candidates who are proactive about their applications!
We think you need these skills to ace PhD Studentship: Predictive Design of Drug Co-Crystals Through Correlative Studies of Inter-Mol[...] in Leeds
Some tips for your application 🫡
Tailor Your Application:Make sure to customise your application to highlight how your skills and experiences align with the project. We want to see your passion for interdisciplinary work, so don’t hold back on showcasing relevant projects or coursework!
Show Off Your Skills:This studentship involves a mix of experimental work and computational analysis. Be sure to mention any experience you have with techniques like Raman spectroscopy or X-ray scattering, as well as any coding or machine learning skills. We love seeing candidates who are eager to learn!
Keep It Clear and Concise:While we appreciate detail, clarity is key! Make sure your application is easy to read and straight to the point. Use bullet points if necessary to break down your achievements and experiences.
Apply Through Our Website:Don’t forget to submit your application through our website! It’s the best way to ensure it gets to us directly. Plus, you’ll find all the info you need about the application process there.
How to prepare for a job interview at University of Leeds
✨Know Your Stuff
Make sure you brush up on the key concepts related to crystallisation, X-ray scattering, and Raman spectroscopy. Familiarise yourself with recent advancements in these areas, as well as how they relate to predictive modelling and machine learning. This will show your passion and understanding of the project.
✨Show Your Interdisciplinary Skills
This role requires a blend of chemistry, physics, and engineering knowledge. Be prepared to discuss how your background and experiences span these fields. Highlight any relevant projects or coursework that demonstrate your ability to work across disciplines.
✨Ask Insightful Questions
Prepare thoughtful questions for Dr Anuradha Pallipurath about the project and its goals. This could include inquiries about the specific techniques you'll be using or how the research fits into broader industry applications. It shows you're genuinely interested and engaged.
✨Demonstrate Your Problem-Solving Skills
Be ready to discuss past challenges you've faced in your studies or projects, particularly those involving data analysis or experimental design. Explain how you approached these problems and what you learned from them, as this will highlight your critical thinking abilities.
