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  Fully Funded Doctoral Studentship in Machine Learning for Enhanced Biopharmaceutical Production


   Department of Engineering

  Dr R Carvalho  Applications accepted all year round  Competition Funded PhD Project (Students Worldwide)

About the Project

Project Title: Fully Funded Doctoral Studentship in Machine Learning for Enhanced Biopharmaceutical Production.

The project aims to improve the development and manufacturing of biologics (monoclonal antibodies and vaccines) by modelling the growth of cells in bioreactors (upstream) and the removal of impurities from the product (downstream). The project has the following objectives:

1. Develop innovative methods for understanding and predicting cell growth and impurity removal in pharmaceutical manufacturing, focusing on biologics such as monoclonal antibodies and vaccines. This includes the study of both the initial growth phase (upstream) and the purification phase (downstream).

2. Use advanced mathematical, statistical, and machine learning techniques to decipher the principles governing these processes, aiming to simplify the complex dynamics of cell growth in bioreactors.

3. Optimise the supply of nutrients to cells during production using reinforcement learning strategies, and enhance the purification process to ensure the final product's purity and efficacy.

4. As part of this PhD studentship, you will be expected to contribute to the body of knowledge in the field by publishing your research findings in high-impact, peer-reviewed academic journals to reach a wide audience and maximise the impact of your work.

This project integrates with CPI's Digital Grand Challenge, a transformative initiative aimed at revolutionising the pharmaceutical industry through digital innovations. Our primary focus is on harnessing machine learning algorithms and statistical models to optimise biologics manufacturing, a goal that aligns with the challenge's broader objectives. These include enhancing patient outcomes, bolstering sustainability and flexibility, and reducing manufacturing costs. As a team member, you will be at the forefront of this exciting industry transformation. Additionally, you will join a growing cohort of PhD students employing statistical machine learning for system identification (see http://ruicarvalho.org).

You will have the opportunity to collaborate with the National Biologics Manufacturing Centre (https://www.uk-cpi.com/about/national-centres/national-biologics-manufacturing-centre) and the Medicines Manufacturing Innovation Centre (https://www.uk-cpi.com/about/national-centres/medicines-manufacturing-innovation-centre). These collaborations aim to address the Digital Grand Challenge, which seeks to leverage the potential of digital technologies in biomanufacturing. More information about the Digital Grand Challenge can be found here: https://www.uk-cpi.com/about/national-centres/digital-creating-the-digital-pharma-factory-of-the-future.

The successful applicant will be working within the Durham Institute for Data Science (https://www.durham.ac.uk/research/institutes-and-centres/data-science/).

Prospective candidates will be judged according to how well they meet the following criteria:

·      First class honours degree in the mathematical sciences, biology, computer science, physics, chemistry, engineering, or a related field.

·      Excellent understanding of statistics and machine learning.

·      Ability to undertake scientific programming in R and Python.

·      Excellent written and spoken communication skills in English.

The Studentship is fully funded for 3.5 years with a tax-free stipend at the EPSRC rate (currently, £18,622 for 2023/24). Full funding is available to UK, or EU nationals who have lived in the UK for three years or more before the course starts. International students can also apply but will have to cover the difference between home and international tuition fees.

In the first instance, interested candidates are encouraged to make an informal enquiry to Dr Rui Carvalho (). To apply formally for this studentship, you need to submit a cover letter highlighting what you can bring to the project, a CV and the names of two academic referees. Applicants will be required to submit a formal application using the online system found at https://www.dur.ac.uk/postgraduate/study/apply/.

The Engineering Department holds an Athena Swan Bronze award, highlighting its commitment to promoting women in Science, Engineering and Technology.

Computer Science (8)

Funding Notes

The Studentship is fully funded for 3.5 years with a tax-free stipend at the EPSRC rate (currently, £18,622 for 2023/24). Full funding is available to UK, or EU nationals who have lived in the UK for three years or more before the course starts. International students can also apply but will have to cover the difference between home and international tuition fees.