To establish and undertake high quality collaborative research, working with colleagues from other disciplines and organisations to deliver projects objectives; to develop, train and demonstrate feasibility of physics informed neural network population balance models for crystallisation processes in pharmaceutical manufacturing.
Salary: £36,024 - £44,263
FTE: 1 FTE
Term:
Fixed
Term (12 months)
Closing Date: 24/06/2024
Applications are invited for a Research Associate position at the CMAC medicines manufacturing research centre (www.cmac.ac.uk), in the development of physics informed neural network population balance models. The CMAC centre comprises a multidisciplinary team of academics and researchers. The centre provides a platform for collaborative research, training, and knowledge exchange in advanced pharmaceutical manufacturing with a vibrant programme of basic and applied research that spans advanced processing of active ingredients and formulated systems, process analysis, modelling, monitoring, and control and product analysis, testing, and characterisation. CMAC is a multicultural, diverse, supportive, and inclusive research environment, and we welcome applications from all sections of the community. We offer agile working options in agreement with managers to meet the needs of the post and personal circumstances to support personal physical and mental well-being, as well as any caring responsibilities.
As part of our programme, we have an exciting opportunity for researchers to work on a cutting-edge project developing and demonstrating the feasibility of pre-trained physics informed neural network population balance models. The project is led by Dr Cameron Brown and is funded by the EPSRC along with project partners: UCB Pharma, Pfizer, Siemens, and Cambridge Crystallographic Data Centre. The project start date is 1st September 2024.
As a Research Associate, you will develop research objectives, conduct individual and collaborative research and contribute to the development of new research methods. You will write up research work for publication, individually or in collaboration with colleagues, and disseminate the results via peer reviewed journal publications, presentation at conferences and software/code repositories. You will join external networks to share information and ideas, and you will collaborate with colleagues on the development of knowledge exchange activities.
To be considered for the role, you will be educated to a minimum of PhD* level in an appropriate discipline (Computer Science, Mathematics, Chemical Engineering, or other relevant discipline), or have significant relevant experience in addition to a relevant degree. You will have sufficient breadth or depth of knowledge in machine learning approaches, neural networks and/or process modelling and population balance modelling, and a developing ability to conduct individual research work and to disseminate results. You will have an ability to plan and organise your own workload effectively and an ability to work within a team environment. You will have excellent interpersonal and communication skills, with the ability to listen, engage and persuade, and to present complex information in an accessible way to a range of audiences.
Whilst not essential for the role, applications are welcomed from candidates with: relevant work experience, membership of relevant Chartered/professional bodies (including the Higher Education Academy), experience of relevant student supervision and teaching activities, and/or experience of knowledge exchange related activities.
*While a Research Associate is ideally sought for this position, applications are welcome from candidates who are close to PhD completion or whose award is pending. In such circumstances, the appointment will be made at Research Assistant level (RS06 salary scale £31,396- £34,980) and duties will be adjusted to reflect the grade of the post. This will continue until the PhD award is confirmed, at which point the duties and grade will be revised accordingly.
Informal enquiries about the post can be directed to Dr Cameron Brown, Principal Investigator (cameron.brown.100@strath.ac.uk).
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