PhD studentship: Developing digital twins to unlock zero carbon and high-value chemical manufacturing (ENG1754)
University of Nottingham
- United Kingdom
- Permanent
- Full-time
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.
- 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.