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Research Fellow in Biopharmaceutical Process Analytics at UCLA
UCL Biochemical Engineering (UCL-BE) is a world leader in bioprocessing, where researchers harness cutting-edge facilities and industrial partnerships to turn breakthroughs in biology into sustainable, real-world innovations, and where exciting careers are being launched. Lonza has been at the forefront of therapeutic protein manufacturing in mammalian cells since the early 1980s and is one of the largest healthcare manufacturing organizations in the world. We are looking for highly motivated researchers to form a team to realize a new paradigm to improve the vital process of autonomous navigation.
About the role
Next-generation biopharmaceuticals are becoming increasingly complex, making them more effective but harder to produce. Delivering such discoveries to patients requires careful evaluation and manufacturing optimization. We have identified an opportunity to combine continuous microfluidic (ÂμF) process models, process analysis technology (PAT) and machine learning (ML) to achieve a paradigm shift in bioprocess optimization across modalities and throughout the process. We will investigate three prototypes for (1) protein aggregation (safety), (2) protein refolding (therapeutic activity), and (3) culture medium optimization (cost-effectiveness). Expected benefits include shorter time to get to the clinic, improved access to life-saving medicines, and enhanced sustainability. The project is co-led by UCL-BE and Lonza, and is funded for three years. We are looking for a highly motivated postdoctoral researcher to join this ambitious thriving partnership between UCL and Lonza and work as part of a multidisciplinary team across both sites (London and Slough). This role will develop optical biophysics modules that can be used in conjunction with microfluidic systems to evaluate protein refolding, aggregation and native content in biopharmaceutical formulations. The position holder will lead the technical development of optical systems, associated data analysis, and integration into high-throughput experimental workflows to accelerate process formulation selection for biopharmaceutical manufacturing. The role will include leading industry feasibility studies with Lonza to demonstrate impact. This role is primarily funded for 24 months, with the possibility of extension.
About you
You will hold a PhD in a relevant discipline such as biochemical engineering, optical physics, protein biophysics, analytical chemistry or any related field. You will bring experience in (bio)optical physics, especially in the design and construction of optical systems for measurement. You will have excellent communication skills and the ability to work independently and collaboratively across teams and organisations. Experience in protein chemistry or structure would be desirable but not essential. Commitment to UCL values ​​and promoting equality, diversity and inclusion is essential.
What we offer
We also offer some great benefits, visit https://www.ucl.ac.uk/work-at-ucl/rewards-and-benefits To learn more.
Our commitment to equality, diversity and inclusion
As a global university in London, we know that diversity fosters creativity and innovation, and we want our community to represent the diversity of global talent. We are committed to equal opportunity, being fair and inclusive, and being a place where we all belong. We therefore particularly encourage applications from candidates who are likely to be under-represented in the UCL workforce. This includes people from black, Asian and minority ethnic backgrounds; People with disabilities People from the LGBTQI+ community; As for our roles in the ninth and tenth grades, they are for women. Our department has received a Gold Athena Swan Award in recognition of our commitment and demonstrated 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/
https://www.jobs.ac.uk/job/DOZ647/research-fellow-in-biopharmaceutical-process-analytics/



