About the Project
Chemistry is undergoing a transformation. Advances in artificial intelligence (AI), automation, and machine learning are changing how we design, synthesize, and analyse molecules. This PhD opportunity will place you at the cutting edge of Digital Supramolecular Chemistry, combining hands-on experimental research with computational and automated tools to accelerate molecular discovery.
Your project will be shaped around your interests and strengths but will be related to the fundamental supramolecular and physical organic chemistry concepts. We have projects available that range from mostly synthetic to mostly computational, with everything in between!
This PhD opportunity is ideal for chemistry students who:
- Enjoy experimental chemistry but are also excited about data-driven approaches.
- Want to develop real-world skills in high-throughput synthesis, machine learning, and automation.
- Are passionate about supramolecular chemistry concepts such as intermolecular interactions, molecular recognition or catalysis.
- Have interest in Python coding and data analysis.
We welcome applicants from all backgrounds, genders, and identities who are excited to explore the
future of chemistry in a supportive and inclusive research environment. Female applicants are particularly invited to apply.
About the project
This PhD focuses on developing data-driven methods for the discovery of next-generation supramolecular systems with real-world applications in sustainability, healthcare, and materials science. The projects lies on an intersection of supramolecular and physical organic chemistry with automation and data science.
Molecular recognition – the ability of one molecule to selectively interact with another – is key to designing highly targeted chemical sensors, pollutant filters, and drug carriers. You will design and synthesize self-assembling supramolecular systems. This will allow us to use AI to predict host-guest interactions faster than traditional methods and guide molecular design, enabling discovery of new separation methods and future catalysts. As supramolecular chemistry relies on precise measurements of how molecules interaction, the automation tools and workflows you will create will improve reproducibility and help us understand the complex behaviour observed in supramolecular systems.
You will gain expertise in:
- Molecular design and synthesis: Creating molecules with tailored functions, such as pollutant capture, drug delivery, smart materials and novel catalysts.
- Dynamic combinatorial chemistry: Exploiting reversible covalent bonds to explore huge regions of the chemical space, with simple building blocks leading to complex adaptive structures.
- AI and computational chemistry: Using machine learning to predict how molecules behave, optimize their structures before synthesis, and understanding their emergent behaviour.
- Self-driving chemistry and laboratory automation: Developing robotic and high-throughput platforms to accelerate experimental workflows and expedite accumulation of high-fidelity data.
Deadline: 31 December 2025
For more information and to apply visit https://www.findaphd.com/phds/project/laboratory-automation-and-ai-for-supramolecular-chemistry-and-molecular-recognition/?p191975
