Adaptive Robotic Chemists for Resilient Pharmaceuticals

Description

The future of small-molecule pharmaceuticals, driven by advancements like generative AI for novel molecule and material design, still critically depends on high-quality data from physical experiments to verify model-based projections. Some aspects of this verification are very process intensive, such as Long Term Stability Testing (LTSS). In this 

process analytical data from multiple storage conditions, multiple packaging systems and multiple batches needs to be generated, in a regulated fashion, over an extended period (e.g. up to three years data needs to be generated). One of the tests that is carried out on all solid dosage forms in LTSS is dissolution. This process is still very much manual and laborious as it does not lend itself well to automation due to the heterogeneity in the process when using different materials and formulations.

In this project, we will explore how intelligent robotic systems can be designed and deployed for dissolution testing using a modular, human-robot collaborative approach. Initially, we will start by focusing on addressing the robotics challenges related to preparing, dispensing and placing the dosage form into the test media, and removing it safely and in a timely manner at the end of the test, ready for the next test. Subsequently, we will integrate statistical frameworks with robust uncertainty estimates, such as conformal predictors, enabling the robot to autonomously determine its next action or request human intervention based on its confidence level. This approach aims not only to accelerate dissolution testing through innovative robotic systems but also to establish reliable, uncertainty-aware methodologies, fostering trust in AI-driven robots for pharmaceuticals. 

This project offers a unique opportunity for the student to:

• Develop intelligent robotic systems capable of adapting to the complexities of heterogeneous materials and formulations in dissolution testing.
• Establish a reliable process in a safety-critical setting by developing a statistical framework with robust uncertainty estimates.
• Deploy and validate the robotics system in real-world labs at the University of Liverpool and in collaboration with our industrial partner, Bristol Myers Squibb (BMS).
• Collaborate with external partners in our collaborative network of ongoing multidisciplinary projects.

The student will work across the research groups of Dr Pizzuto (Computer Science) and Dr Bradley (Chemistry) and contribute towards cutting-edge robotics research in AI-driven robotic scientists at the University of Liverpool, focused on their deployment in real-world applications. They will also have the opportunity to collaborate with external partners on our ongoing multidisciplinary projects.

This project is offered under the University of Liverpool EPSRC Centre for Doctoral Training in Digital and Automated Materials Chemistry along with other studentships for students from backgrounds spanning the physical and computer sciences to start in October 2025. These students will develop core expertise in robotic, digital, chemical and physical thinking, which they will apply in their domain-specific research in materials design, discovery and processing. By working with each other and benefiting from a tailored training programme they will become both leaders and fully participating team players, aware of the best practices in inclusive and diverse R&D environments.

Applicants are advised to apply as soon as possible no later than 25^th May 2025. We will review applications as they come in. The position will be closed when suitable candidate has been identified.

Please review our guide on “How to Apply” carefully and complete the online postgraduate research application form to apply for this PhD project.

We strongly encourage applicants to get in touch with the supervisory team to get a better idea of the project.

We want all our Staff and Students to feel that Liverpool is an inclusive and welcoming environment that actively celebrates and encourages diversity. We are committed to working with students to make all reasonable project adaptations including supporting those with caring responsibilities, disabilities or other personal circumstances. For example, if you have a disability you may be entitled to a Disabled Students Allowance on top of your studentship to help cover the costs of any additional support that a person studying for a doctorate might need as a result.