At a Glance
- Tasks: Lead an interdisciplinary project using machine learning to design particle properties.
- Company: Collaborate with top researchers at Leeds and the Cambridge Crystallographic Data Centre.
- Benefits: Gain valuable skills in data science and machine learning while contributing to innovative research.
- Other info: Exciting opportunity for career growth in a dynamic research environment.
- Why this job: Make a real impact in industries like pharmaceuticals and cosmetics through cutting-edge technology.
- Qualifications: First class or upper second class degree in relevant fields like Chemical Engineering or AI.
The predicted salary is between 18000 - 25000 £ per year.
Lead Supervisor’s full name & email address: Dr Anuradha Pallipurath a.r.pallipurath@leeds.ac.uk
Co-supervisor name(s) & email address(s): 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. Being able to predictively design particle properties is of great economic value and is applicable to a range of industries such as pharmaceuticals, agrochemical, additives, cosmetics and food.
This project aims to develop machine learning models to predict a particle shape and size for a given chemical formulae and crystallisation method. Extractive Language learning models developed will be able to understand crystallographic information from the big data available in the CSD and enable future applications in considering other particle properties. This project will enhance the understanding of the value of metadata that could be associated with structural information and will help define the standards required for crystal structure data curation necessary to deliver Materials 4.0.
The project will combine data science and structural science work with researchers at Leeds and at the Cambridge Crystallographic Data Centre, and will involve development of Large language modelling to process metadata from structural information. You will also have an opportunity to learn machine learning methods for the analysis of structural information with a view to predict particle properties. You will be funded by the Royce CDT and the Cambridge Crystallographic Data Centre.
References: None
Please state your entry requirements plus any necessary or desired background: First class or an upper second class British Bachelors Honours degree (or equivalent) in an appropriate discipline.
Subject Area: Chemical Engineering, Materials Science, Pharmaceutical/Medicinal Chemistry, Physical Chemistry, Artificial Intelligence, Machine Learning.
PhD Studentship: Particle Properties by Design in Leeds employer: University of Leeds
As a leading research institution, we offer PhD candidates the chance to engage in groundbreaking interdisciplinary projects that bridge chemistry, physics, and engineering. Our collaborative work culture fosters innovation and personal growth, providing access to state-of-the-art resources and mentorship from esteemed researchers. Located in a vibrant academic community, this role not only enhances your technical skills but also opens doors to diverse career opportunities across various industries.
StudySmarter Expert Advice🤫
We think this is how you could land PhD Studentship: Particle Properties by Design in Leeds
✨Tip Number 1
Network like a pro! Reach out to current or former PhD students in similar fields, especially those who have worked with Dr Anuradha Pallipurath. A friendly chat can give you insider info and maybe even a foot in the door.
✨Tip Number 2
Show off your skills! Prepare a portfolio or a presentation that highlights your experience with machine learning and data science. This will help you stand out during interviews and show how you can contribute to the project.
✨Tip Number 3
Practice makes perfect! Conduct mock interviews with friends or mentors. Focus on explaining complex concepts in simple terms, as you'll need to communicate effectively with a diverse team of researchers.
✨Tip Number 4
Apply through our website! It’s the best way to ensure your application gets seen. Plus, we love seeing candidates who take the initiative to engage directly with us.
We think you need these skills to ace PhD Studentship: Particle Properties by Design in Leeds
Some tips for your application 🫡
Tailor Your CV:Make sure your CV reflects the skills and experiences that are relevant to the PhD studentship. Highlight any projects or coursework related to chemistry, physics, engineering, or machine learning. We want to see how you fit into our interdisciplinary project!
Craft a Compelling Cover Letter:Your cover letter is your chance to shine! Explain why you're passionate about this project and how your background aligns with our goals. Be sure to mention any specific experiences that demonstrate your ability to work with data science and structural information.
Showcase Your Research Interests:In your application, let us know what excites you about particle properties and machine learning. Share any relevant research you've done or topics you're keen to explore. This helps us see your enthusiasm and potential contributions to the team!
Apply Through Our Website:We encourage you to submit your application through our website for a smoother process. It’s the best way to ensure your application gets the attention it deserves. Plus, it shows you’re serious about joining our team at StudySmarter!
How to prepare for a job interview at University of Leeds
✨Know Your Stuff
Make sure you have a solid understanding of the project’s focus areas, like machine learning and particle properties. Brush up on relevant concepts in chemistry, physics, and engineering, as well as any recent advancements in these fields. This will help you engage in meaningful discussions during the interview.
✨Showcase Your Skills
Prepare to discuss your previous experience and how it relates to the project. Highlight any relevant coursework, research, or projects that demonstrate your ability to work with data science and structural science. Be ready to explain how your skills can contribute to predicting particle properties.
✨Ask Insightful Questions
Interviews are a two-way street! Prepare thoughtful questions about the project, the team, and the expectations for the role. This shows your genuine interest and helps you assess if the position aligns with your career goals.
✨Be Yourself
While it's important to be professional, don’t forget to let your personality shine through. The supervisors are looking for someone who fits well within their team, so being authentic can make a positive impression. Share your passion for the subject and your enthusiasm for the opportunity!
