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- Tasks: Dive into machine learning and organic synthesis to tackle complex natural product challenges.
- Company: Join the University of Edinburgh's School of Chemistry, a leader in innovative research.
- Benefits: Enjoy a fully funded studentship with tuition coverage and a stipend of £20,780 per annum.
- Why this job: Gain unique expertise in total synthesis and deep machine learning while contributing to impactful research.
- Qualifications: Ideal candidates should have a passion for organic synthesis and machine learning, with some research experience.
- Other info: Be part of a diverse and inclusive environment committed to equality and family-friendly initiatives.
The predicted salary is between 20780 - 20780 £ per year.
Machine Learning-Guided Synthetic Strategies for Natural Product Synthesis
Natural products serve as a rich source of therapeutic agents, but their structural complexity presents challenges in synthesis. However, this complexity also allowed these scaffolds to become fruitful environments for new chemical discovery, providing rigorous \”testing grounds\” for methodology. In turn, this yielded better syntheses to a wider variety of targets.
Machine learning (ML) has seen advances in synthesis prediction and planning; however, ML methods are typically built for, and applied to, simpler molecules. ML methodology forged in the intricate chemical setting of natural product synthesis will yield streamlined syntheses whilst also advancing the field of data-driven chemistry. In conjunction with the novel ML development, the student will be guiding model training with experimental validation in the lab. This work builds upon the work from Prof. Lawrence\’s group in biomimetic total synthesis and Dr. King-Smith\’s work in data-driven chemistry.
Candidates should have a keen interest in both organic synthesis and machine learning with some research experience in either field. This is an interdisciplinary post and will provide an unparalleled opportunity for a student to gain expertise in total synthesis and deep machine learning.
How to apply
In the first instance, the initial application of cover letter and CV should be directed to:
Dr. Emma King-Smith
School of Chemistry, University of Edinburgh,
David Brewster Road,
Edinburgh EH9 3FJ, UK.
The form will automatically generate a unique ‘Response ID number’ that you must include in your cover letter.
Equality and Diversity
The School of Chemistry holds a Silver Athena SWAN award in recognition of our commitment to advance gender equality in higher education. The University is a member of the Race Equality Charter and is a Stonewall Scotland Diversity Champion, actively promoting LGBT equality.
The University has a range of initiatives to support a family-friendly working environment.
The studentship is fully funded for 42 months by the University of Edinburgh and covers tuition fees and an annual stipend at the UKRI rate, for 2025-26 this is £20,780 per annum, for a candidate satisfying EPSRC residency criteria.
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Machine Learning-Guided Synthetic Strategies for Natural product Synthesis employer: FindAPhD
Contact Detail:
FindAPhD Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Machine Learning-Guided Synthetic Strategies for Natural product Synthesis
✨Tip Number 1
Familiarise yourself with the latest advancements in machine learning as they apply to organic synthesis. Understanding how ML can be integrated into natural product synthesis will not only enhance your knowledge but also demonstrate your genuine interest in the field during discussions.
✨Tip Number 2
Engage with current research and publications related to data-driven chemistry and synthetic strategies. This will help you speak confidently about relevant topics and show that you are proactive in keeping up with industry trends, which is crucial for this interdisciplinary role.
✨Tip Number 3
Network with professionals in both organic synthesis and machine learning. Attend seminars, workshops, or online webinars where you can meet experts in these fields. Building connections can provide insights and potentially lead to recommendations when applying for the position.
✨Tip Number 4
Prepare to discuss your research experience in detail, especially any projects that involved either organic synthesis or machine learning. Be ready to explain your contributions and what you learned, as this will showcase your hands-on experience and readiness for the challenges of the role.
We think you need these skills to ace Machine Learning-Guided Synthetic Strategies for Natural product Synthesis
Some tips for your application 🫡
Understand the Role: Before applying, make sure to thoroughly read the job description. Understand the key responsibilities and required skills, particularly in organic synthesis and machine learning, to tailor your application effectively.
Craft a Tailored Cover Letter: Write a cover letter that highlights your relevant experience in both organic synthesis and machine learning. Mention specific projects or research that align with the role and express your enthusiasm for the interdisciplinary nature of the position.
Highlight Relevant Experience: In your CV, focus on showcasing any research experience related to natural product synthesis or machine learning. Include specific examples of your contributions and the outcomes of your work to demonstrate your capabilities.
Include the Response ID: Remember to include the unique ‘Response ID number’ generated during your application process in your cover letter. This is crucial for ensuring your application is processed correctly.
How to prepare for a job interview at FindAPhD
✨Show Your Passion for Organic Synthesis and Machine Learning
Make sure to express your genuine interest in both organic synthesis and machine learning during the interview. Share any relevant experiences or projects that highlight your enthusiasm and understanding of these fields.
✨Prepare for Technical Questions
Expect to face technical questions related to both organic chemistry and machine learning. Brush up on key concepts, methodologies, and recent advancements in these areas to demonstrate your knowledge and readiness for the role.
✨Discuss Your Research Experience
Be ready to talk about your previous research experience, especially if it relates to either organic synthesis or machine learning. Highlight specific projects, your contributions, and what you learned from those experiences.
✨Ask Insightful Questions
Prepare thoughtful questions to ask the interviewers about their work, the team dynamics, and future projects. This shows your interest in the position and helps you gauge if the environment is a good fit for you.
