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- Tasks: Develop AI models to predict chemical reactivity and assess prediction uncertainty.
- Company: Join a leading research group focused on sustainable chemistry and innovative AI solutions.
- Benefits: Enjoy a tax-free stipend, fully-funded tuition, and the chance to work on cutting-edge technology.
- Why this job: Make a real impact in green chemistry while collaborating with experts in AI and chemistry.
- Qualifications: Must have a 2:1 Honours degree in Chemistry, Mathematics, or related fields; programming skills required.
- Other info: Open to home students only; application deadline is 5th May 2025.
The predicted salary is between 20780 - 20780 £ per year.
In this PhD project, we will develop and implement approaches for estimating the uncertainty in AI predictions of chemical reactivity, to help strengthen the interaction between human chemists and machine learning algorithms and to assess when AI predictions are likely to be correct and when, for example, first principles quantum chemical calculations might be helpful.
Predicting chemical reactivity is, in general, a challenging problem and one for which there is relatively little data, because experimental chemistry takes time and is expensive. Within our research group, we have a highly automated workflow for high-level quantum chemical calculations and we have generated thousands of examples relating to the reactivity of molecules for a specific chemical reaction.
This project will evaluate a variety of machine learning algorithms trained on these data and, most crucially, will develop and implement techniques for computing the uncertainty in the prediction. The algorithms developed in the project will be implemented in our ai4green electronic lab notebook, which is available as a web-based application: http://ai4green.app and which is the focus of a major ongoing project supported by the Royal Academy of Engineering.
The results of the project will help chemists to make molecules in a greener and more sustainable fashion, by identifying routes with fewer steps or routes involving more benign reagents.
Applicants should have, or expected to achieve, at least a 2:1 Honours degree (or equivalent if from other countries) in Chemistry or Mathematics or a related subject. A MChem/MSc-4-year integrated Masters, a BSc + MSc or a BSc with substantial research experience will be highly advantageous. Experience in computer programming will be essential.
The studentship is open to home students only. The deadline for a formal application is 5th May. Start date: 1st Oct 2025. Annual tax-free stipend based on the UKRI rate (currently £20,780) plus fully-funded PhD tuition fees for the 3.5 years. For further details and to arrange an interview please contact Jonathan Hirst (School of Chemistry).
PhD Studentship: Uncertainty quantification for machine learning models of chemical reactivity employer: University Of Nottingham
Contact Detail:
University Of Nottingham Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land PhD Studentship: Uncertainty quantification for machine learning models of chemical reactivity
✨Tip Number 1
Familiarise yourself with the latest advancements in machine learning algorithms, particularly those used in chemical reactivity. This knowledge will not only help you during interviews but also demonstrate your genuine interest in the field.
✨Tip Number 2
Engage with the research community by attending relevant conferences or webinars. Networking with professionals in the field can provide insights and potentially lead to recommendations that could strengthen your application.
✨Tip Number 3
Showcase any programming experience you have, especially in languages commonly used in machine learning, such as Python. Being able to discuss your projects or contributions can set you apart from other candidates.
✨Tip Number 4
Reach out to Jonathan Hirst or other faculty members involved in the project to express your interest and ask insightful questions. This proactive approach can leave a positive impression and may give you an edge in the selection process.
We think you need these skills to ace PhD Studentship: Uncertainty quantification for machine learning models of chemical reactivity
Some tips for your application 🫡
Understand the Project: Read the job description thoroughly to grasp the project's objectives and requirements. Familiarise yourself with terms like 'uncertainty quantification' and 'machine learning models of chemical reactivity' to tailor your application effectively.
Highlight Relevant Experience: In your CV and cover letter, emphasise any relevant academic qualifications, such as your degree in Chemistry or Mathematics, and any research experience. Mention specific programming skills that relate to the project, as this is essential for the role.
Craft a Strong Personal Statement: Write a compelling personal statement that outlines your motivation for applying, your interest in the intersection of chemistry and machine learning, and how your background makes you a suitable candidate for this PhD studentship.
Check Application Requirements: Ensure you have all necessary documents ready, including your CV, personal statement, and any transcripts. Pay attention to the closing date (5th May 2025) and ensure your application is submitted well before this deadline.
How to prepare for a job interview at University Of Nottingham
✨Showcase Your Technical Skills
Make sure to highlight your programming experience and any relevant projects you've worked on. Discuss specific programming languages or tools you are proficient in, especially those related to machine learning and data analysis.
✨Demonstrate Your Understanding of Chemical Reactivity
Prepare to discuss your knowledge of chemical reactivity and how it relates to machine learning. Be ready to explain concepts clearly and how they can be applied in practical scenarios, particularly in the context of the project.
✨Prepare Questions About the Project
Show your enthusiasm by preparing insightful questions about the PhD project and its goals. This demonstrates your genuine interest and helps you understand how you can contribute effectively.
✨Familiarise Yourself with AI4Green
Take some time to explore the ai4green electronic lab notebook. Understanding its functionalities and how your work will integrate with it can give you an edge during the interview.
