Research Associate (Fixed Term)

The Sustainable Reaction Engineering group, headed by Prof. Alexei Lapkin (lapkingroup.com), is looking for an outstanding postdoctoral research associate for a project focused on development of novel AI methods for pharmaceutical processes development and optimisation.

Project Description

Development of synthesis and optimisation of reactions remain rate-limiting factors in pharmaceutical process development, often relying on resource-intensive trial-and-error approaches that are costly, time-consuming, and wasteful. This highlights the need to develop new digital methods that are capable of rapidly responding to emerging health challenges. In this EPSRC funded project, we will combine expertise across the Universities of Leeds (Dr Adam Clayton, Prof. Richard Bourne), Liverpool (Prof. Anna Slater) and Cambridge (Prof. Alexei Lapkin) to create a network of digitally coupled reactors, capable of high-throughput screening and self-optimising manufacturing processes. This will be achieved by combining different flow reactor technologies, analytical techniques, and automated workflows to provide enhanced mapping of chemical space and generation of robust high-quality datasets. Modular experimental platforms will be designed, capable of efficiently exploring complex mixed variable design spaces on the microlitre scale. Our multisite reactor network will be driven by next generation machine learning algorithms, which will use knowledge from prior experimental campaigns to increase library synthesis success rates and accelerate the development and optimisation of chemically related processes. In collaboration with our partners in the pharmaceutical industry, we will leverage this novel workflow to accelerate lower cost and more sustainable manufacturing of future medicines.

Specific Post Requirements

The role holder will be responsible for leading the method development part of the project, focusing on ML algorithms to support process development. Prior experience in process development, machine learning, first principle process modelling, digital twins is needed for this project. The role holder is expected to interact with academic and industry collaborators and disseminate results regularly across the involved research teams, delivering presentations, seminars and occasional talks when required at national and international conferences. To publish papers on the research project and present results at national and international conferences. It is expected that there will be IP generated within the project.

The successful candidate will have a PhD, or studying towards a PhD, in PSE with emphasis on machine learning methods, good working knowledge of existing approaches to Bayesian process optimisation. Subject matter knowledge in chemical reaction engineering is essential. The ability to work both as part of a team, and independently, coupled with excellent communication, organisational and problem-solving skills are essential to this role. More information on the role and person specification is available on the Further Particulars attached. Appointment at Research Associate level is dependent on having a PhD. Those who have submitted but not yet received their PhD will be appointed at Research Assistant level, which will be amended to Research Associate once the PhD has been awarded.

Fixed-term: The funds for this post are available for 3 years in the first instance.

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Informal enquiries are welcomed and should be directed to Mr Vito Candela, HR Administrator, at [email protected] .

Applications closing dates 7th June 2024

Please quote reference NQ41787 on your application and in any correspondence about this vacancy.

The University actively supports equality, diversity and inclusion and encourages applications from all sections of society.

The University has a responsibility to ensure that all employees are eligible to live and work in the UK.

Further information
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