At a Glance
- Tasks: Lead an interdisciplinary project using AI to predict particle properties across various industries.
- Company: Collaborative research environment with top institutions like Leeds and Cambridge.
- Benefits: Gain hands-on experience in machine learning and data science with funding support.
- Other info: Exciting opportunity for career growth in a dynamic, innovative research setting.
- Why this job: Make a real impact in cutting-edge research that shapes the future of materials science.
- Qualifications: First class or upper second class degree in relevant fields like Chemical Engineering or AI.
The predicted salary is between 28000 - 35000 £ 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 in AI-Driven Design of Particle Properties in Leeds employer: University of Leeds
As a leading institution in interdisciplinary research, this opportunity offers an exceptional environment for aspiring scientists and engineers to thrive. With access to cutting-edge resources and collaboration with esteemed researchers at Leeds and the Cambridge Crystallographic Data Centre, employees can expect a vibrant work culture that fosters innovation and professional growth. The project not only enhances technical skills in machine learning and data science but also contributes to significant advancements across various industries, making it a truly rewarding career path.
StudySmarter Expert Advice🤫
We think this is how you could land PhD in AI-Driven Design of Particle Properties in Leeds
✨Tip Number 1
Network like a pro! Reach out to professionals in the field of AI and particle properties. Attend relevant conferences or webinars, and don’t be shy to slide into DMs on LinkedIn. We all know that sometimes it’s not just what you know, but who you know!
✨Tip Number 2
Prepare for interviews by practising common questions related to machine learning and particle properties. We recommend doing mock interviews with friends or mentors. The more you practice, the more confident you'll feel when it’s your turn to shine!
✨Tip Number 3
Showcase your skills! Create a portfolio that highlights any projects or research you've done related to AI and materials science. We love seeing practical applications of your knowledge, so make sure to include any relevant work that demonstrates your expertise.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen. Plus, we often have exclusive opportunities listed there that you won’t find anywhere else. So, get clicking and let’s land that dream job together!
We think you need these skills to ace PhD in AI-Driven Design of Particle Properties in Leeds
Some tips for your application 🫡
Tailor Your CV:Make sure your CV is tailored to highlight your relevant skills and experiences related to AI, machine learning, and particle properties. We want to see how your background fits with the interdisciplinary nature of the project!
Craft a Compelling Cover Letter:Your cover letter is your chance to shine! Use it to explain why you're passionate about this project and how your unique skills can contribute. We love seeing enthusiasm and a clear connection to the role.
Showcase Relevant Projects:If you've worked on any projects that involve data science, machine learning, or materials science, be sure to mention them. We’re keen to see practical examples of your work that align with the project goals.
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 University of Leeds
✨Know Your Stuff
Make sure you brush up on the fundamentals of AI, machine learning, and particle properties. Familiarise yourself with recent advancements in these areas, especially how they relate to chemistry and engineering. This will not only show your passion but also your readiness to dive into the project.
✨Showcase Your Skills
Prepare to discuss any relevant projects or experiences that highlight your skills in data science and structural analysis. Be ready to explain how you've applied machine learning methods in past work or studies, as this will demonstrate your practical knowledge and problem-solving abilities.
✨Ask Insightful Questions
Come prepared with thoughtful questions about the project and its applications. Inquire about the collaboration between Leeds and the Cambridge Crystallographic Data Centre, or ask about the specific challenges they face in predicting particle properties. This shows your genuine interest and engagement with the role.
✨Connect with Your Interviewers
Take a moment to research Dr Anuradha Pallipurath and any co-supervisors. Understanding their work and interests can help you build rapport during the interview. A personal connection can make a lasting impression and set you apart from other candidates.
