PhD studentship: Developing digital twins to unlock zero carbon and high-value chemical manufacturing (ENG1754)

Job title:

PhD studentship: Developing digital twins to unlock zero carbon and high-value chemical manufacturing (ENG1754)

Company:

University of Nottingham

Job description

Location: UK Other
Closing Date: Friday 31 May 2024
Reference: ENG1754Applications are invited for a three-year PhD studentship within the Food, Water, Waste Research Group in the Faculty of Engineering, focusing on harnessing the power of digital twins to revolutionise bioprocesses for waste treatment and high-value chemical production.Vision and MotivationThe chemical sector supplies materials for approximately 95% of the products manufactured today. As of 2020, 84.5% of the chemical sector’s feedstocks come from fossil-based resources, and the sector’s reliance on fossil carbon for its feedstock and energy needs makes it the UK’s second-highest industrial emitter. An urgent need exists for a circular economy approach that transforms waste into high-value chemical products through bioprocesses such as anaerobic digestion, microbial electrolysis cell, and fermentation, paving the way towards a sustainable and net-zero future.In today’s rapidly evolving technological landscape, digital twins represent a paradigm shift in bioprocess engineering by offering real-time, predictive simulations of complex biological systems. As a PhD candidate, you’ll be at the forefront of innovation, developing and implementing digital twins to simulate, optimise, and accelerate bioprocess development. From microbial engineering to process optimisation, your work will redefine the landscape of sustainable biotechnology.What You’ll Do

• Design and develop digital twins tailored to simulate biotechnological systems for waste treatment and high-value chemical production.
• Integrate advanced hybrid modelling techniques with real-time data streams to create dynamic, predictive simulations.
• Collaborate with interdisciplinary teams to validate and optimise digital twin models using experimental data.
• Drive innovation by exploring novel applications of digital twin technology in bioprocess engineering.
• Publish findings in top-tier journals and present at international conferences, establishing yourself as a thought leader in the field.

Who We Are Looking For

• First-class or equivalent in chemical engineering, biotechnology, computer science, mathematics, or a related discipline. A 2:1 may also be considered if other criteria are met.
• Proficiency in programming languages (Python/MATLAB) commonly used in machine learning applications is desirable but learning can be completed during the PhD.
• Excellent communication skills and a demonstrated ability to work collaboratively in a multidisciplinary research environment.
• Passion for sustainability and a drive to make a meaningful impact through scientific research.

Funding SupportFully funded studentship, which includes a minimum tax-free stipend of £19,237, is competitively available for home fee-eligible students for an October 2024 start. Note that the funding associated with this role is awarded via an internal competition and is therefore only confirmed after the admission application is approved.Application ProcessInterested candidates should submit a CV, cover letter outlining their research interests and relevant experience to Dr. Oliver Fisher directly (oliver.fisher@nottingham.ac.uk). Informal inquiries are also welcome. Applications will be reviewed on a rolling basis, and the position will be filled as soon as a suitable person has been found; candidates are encouraged to apply as soon as possible.The University actively supports equality, diversity, and inclusion and encourages applications from all sections of society. The Faculty of Engineering provides a thriving working environment for all PGRs, creating a strong sense of community across research disciplines. Community and research culture are important to our PGRs, and the FoE supports this by working closely with our Postgraduate Research Society (PGES) and our PGR Research Group Reps to enhance the research environment for PGRs. PGRs benefit from training through the Researcher Academy’s Training Programme, and those based within the Faculty of Engineering have access to bespoke courses developed for Engineering PGRs, including sessions on paper writing, networking, and career development after the PhD. The Faculty has outstanding facilities and works in partnership with leading industrial partners.

Expected salary

Location

United Kingdom

Job date

Fri, 01 Mar 2024 08:39:47 GMT

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