Project DescriptionThis project aims to improve biologics manufacturing by applying machine learning and statistical techniques to optimise downstream purification processes. The research will focus on analysing chromatography and mass spectrometry data to enhance the removal of impurities from monoclonal antibodies and vaccines.Key objectives include:1. Develop innovative methods for understanding and predicting impurity removal in the downstream processing of biologics, focusing on chromatography and mass spectrometry data analysis.2. Apply advanced mathematical, statistical, and machine learning techniques to decipher the principles governing chromatography processes, with the goal of automating downstream processing.3. Analyze complex multivariate datasets from chromatography experiments and mass spectrometry data.4. Develop predictive models to optimize chromatography conditions and improve the separation of target proteins from impurities.5. Contribute to the body of knowledge in the field by publishing 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 research aligns with the challenge's objectives of enhancing patient outcomes, improving 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 cohort of PhD students employing statistical machine learning for system identification (see http://ruicarvalho.org).Research EnvironmentYou will have the unique opportunity to collaborate with the National Biologics Manufacturing Centre (https://www.uk-cpi.com/about/national-centres/national-biologics-manufacturing-centre), part of Centre for Process Innovation (CPI). The Centre is conveniently located in Darlington, just a 15-minute train ride from Durham, facilitating easy access and frequent collaboration opportunities. This proximity allows for regular on-site visits and exposure to state-of-the-art biologics manufacturing processes, providing valuable insights into the industrial applications of the research.The collaboration addresses CPI's Smart Bioprocess Development Grand Challenge, which aims to leverage digital technologies in biomanufacturing. You will work closely with industry experts and have access to real-world data and challenges in biologics manufacturing. This partnership provides a unique blend of academic research and industry-relevant experience, preparing you for a successful career in either academia or the biopharmaceutical industry.  For more information, visit: https://www.uk-cpi.com/news/cpi-to-develop-digital-bioprocess-modelling-techniques-to-help-bring-innovative-treatments-to-market-fasterThe successful applicant will work within the Durham Institute for Data Science (https://www.durham.ac.uk/research/institutes-and-centres/data-science/) and benefit from its interdisciplinary research environment.Candidate RequirementsProspective candidates will be judged according to how well they meet the following criteria:·      First class honours degree in computer science, mathematical sciences, chemistry, physics, biology, engineering, or a related field.·      Excellent understanding of statistics and machine learning.·      Strong programming skills in R and Python.·      Familiarity with or willingness to learn about chromatography, mass spectrometry, and biopharmaceutical manufacturing processes.·      Excellent written and spoken communication skills in English.·      Ability to work in an interdisciplinary environment, combining expertise in machine learning with applications in biotechnology.Funding DetailsThe Studentship is fully funded for 3.5 years and includes a tax-free stipend at the EPSRC rate (currently, £19,237 for 2024/25). Full funding is available to UK students. EU and international students are eligible to apply but may need to cover the difference between home and international tuition fees. Please contact us for more information on international student funding.How to ApplyInterested candidates are encouraged to make an informal enquiry to Dr Rui Carvalho ([email protected]). To apply formally, submit:1.      A cover letter highlighting your relevant skills and experience2.      CV 3.      Names of two academic referees. Applicants will need to submit a formal application using the online system at: https://www.dur.ac.uk/postgraduate/study/apply/.The Engineering Department holds an Athena Swan Bronze award, highlighting its commitment to promoting diversity in Science, Engineering and Technology.