At a Glance
- Tasks: Conduct groundbreaking research in crystallisation using advanced techniques and machine learning.
- Company: Join a leading academic institution with a focus on interdisciplinary science.
- Benefits: Receive a tax-free maintenance grant of £19,237 per year for 3.5 years.
- Other info: Collaborate with top researchers and gain hands-on experience in cutting-edge technology.
- Why this job: Make a real impact in drug development and process control through innovative research.
- Qualifications: First or Upper Second Class UK Bachelor (Honours) 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: Interdisciplinary Crystallisation & ML in Leeds employer: University of Leeds
As a leading institution in interdisciplinary research, this PhD studentship offers an exceptional opportunity for UK applicants to engage in cutting-edge science at the intersection of chemistry, physics, and engineering. With a supportive work culture that fosters collaboration and innovation, students will benefit from access to state-of-the-art facilities, including the UK's national synchrotron radiation facility, and receive a generous maintenance grant to support their studies. This role not only provides a platform for personal and professional growth through advanced training in machine learning and experimental techniques but also contributes to meaningful advancements in drug crystallisation processes.
StudySmarter Expert Advice🤫
We think this is how you could land PhD Studentship: Interdisciplinary Crystallisation & ML 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, it’s all about who you know!
✨Tip Number 2
Show off your skills! Prepare a portfolio showcasing any relevant projects or research you've done, especially those involving crystallisation or machine learning. This will help you stand out during interviews and discussions.
✨Tip Number 3
Practice makes perfect! Conduct mock interviews with friends or mentors to get comfortable discussing your research interests and how they align with the studentship. The more you practice, the more confident you'll feel!
✨Tip Number 4
Apply through our website! We’ve got loads of resources to help you with your application process. Plus, it shows you're serious about joining our community at StudySmarter. Don’t miss out on this opportunity!
We think you need these skills to ace PhD Studentship: Interdisciplinary Crystallisation & ML in Leeds
Some tips for your application 🫡
Tailor Your CV:Make sure your CV is tailored to the PhD studentship. Highlight relevant experience in analytical chemistry, chemical engineering, or any related fields. We want to see how your background aligns with the exciting interdisciplinary project!
Craft a Compelling Cover Letter:Your cover letter is your chance to shine! Share your passion for crystallisation and machine learning, and explain why this project excites you. We love seeing enthusiasm and a clear understanding of the research area.
Showcase Your Skills:Don’t forget to mention any technical skills you have, especially in Raman spectroscopy, X-ray scattering, or programming. If you've dabbled in machine learning, let us know! We’re keen on candidates who can bring diverse skills to the table.
Apply Through Our Website:We encourage you to apply through our website for a smooth application process. It’s the best way to ensure your application gets the attention it deserves. Plus, it’s super easy!
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 machine learning. Familiarise yourself with recent advancements in these areas, especially how they relate to drug molecules. This will show your passion and understanding of the project.
✨Connect with the Supervisors
Reach out to Dr Anuradha Pallipurath before the interview. Ask insightful questions about the project or express your enthusiasm for the research. This not only demonstrates your interest but also helps you build rapport with your potential supervisor.
✨Showcase Your Skills
Prepare to discuss any relevant experience you have in analytical chemistry, chemical engineering, or computational modelling. Be ready to share specific examples of projects or coursework that highlight your skills in these areas, particularly any hands-on experience with Raman spectroscopy or machine learning.
✨Practice Problem-Solving
Expect some technical questions or problem-solving scenarios during the interview. Practice explaining your thought process clearly and logically. This will help you demonstrate your analytical skills and ability to tackle complex scientific challenges.
