Research Fellow: Digital Twin & PAT for Biomanufacturing (DSP)

About us

The “Next-Gen Biopharma Manufacturing 5.0” Prosperity Partnership is a 5-year collaborative programme between UCL Biochemical Engineering and AstraZeneca, jointly funded by EPSRC and AstraZeneca. It aims to transform antibody manufacturing using AI, automation, digital twins and advanced analytics to improve production of therapies for cancer, obesity and autoimmune conditions. Delivered through the UCL–AstraZeneca Centre of Excellence, the programme will appoint six postdoctoral researchers to work across UCL and AstraZeneca sites, combining computational and experimental approaches. UCL Biochemical Engineering is a global leader in bioprocess engineering research and education, with a mission to develop innovative biomanufacturing solutions for health and a sustainable bioeconomy. The department combines world-class facilities, strong industrial links, and an interdisciplinary research culture. AstraZeneca is a glob al, science-led biopharmaceutical company with a focus on the discovery, development, and commercialisation of medicines in Oncology, Rare Diseases, and BioPharmaceuticals.

About the role

We are seeking a highly motivated postdoctoral researcher to join this ambitious Prosperity Partnership between UCL and AstraZeneca and to work as part of a cross-disciplinary team across both sites (London and Cambridge). This role will contribute to the development of advanced digital twins for downstream bioprocessing, integrating real-time analytics and process analytical technologies (PAT) to accelerate innovation in biopharmaceutical manufacturing. The postholder will lead on the construction and application of mechanistic and hybrid digital twin models for chromatography and tangential flow filtration (TFF) operations, including prediction of binding capacity, multicomponent adsorption, and fouling. These models will incorporate real-time PAT data streams to enable predictiv e monitoring, dynamic process adjustments, and improved control strategies. Working closely with AstraZeneca, the postholder will apply these digital twins and sensors to real-world case studies of novel biologic modalities, evaluating how hybrid PAT–digital twin frameworks can optimise critical quality attributes, reduce development timelines, and de-risk scale-up. This role is funded for 2 years in the first instance, with potential for extension.

About you

You will hold (or be near completion of) a PhD in a relevant discipline such as biochemical engineering, chemical engineering, process systems engineering, computer science, or a related field. You will bring expertise in digital twin development for upstream bioprocessing, with strong programming skills and practical modelling experience. Familiarity with the challenges of upstream biomanufacturing, including process analytical technologies (PAT) and advanced control strategies, would be desirable. You will have excellent communication skills and the ability to work both independently and collaboratively across teams and institutions. A commitment to UCL’s values and to promoting equality, diversity and inclusion is essential.

What we offer

As well as exciting opportunities this role presents, we also offer some great benefits, visit https://www.ucl.ac.uk/work-at-ucl/rewards-and-benefits to find out more.

Our commitment to Equality, Diversity and Inclusion

As London’s Global University, we know diversity fosters creativity and innovation, and we want our community to represent the diversity of the world’s talent. We are committed to equality of opportunity, to being fair and inclusive, and to being a place where we all belong. We therefore particularly encourage applications from candidates who are likely to be underrepresented in UCL’s workforce. Our department holds an Athena Swan Gold award in recognition of our comm itment and demonstrable impact in advancing gender equality. You can read more about our commitment to Equality, Diversity, and Inclusion here: https://www.ucl.ac.uk/equality-diversity-inclusion/

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