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AI-driven Sustainable Food Waste Solutions (SLS3)


   School of Life Sciences

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  Dr Dipankar Sengupta  No more applications being accepted  Funded PhD Project (Students Worldwide)

About the Project

To sustain the burden of unprecedented population growth, the UN has chartered the Sustainable Development Goals (SDG) intended to be, "a blueprint to achieve a better and more sustainable future for all". A key goal among the SDGs is ensuring no hunger - by attaining food security, improving nutrition, and promoting sustainable agriculture. The major hindrance in the path of accomplishing this goal is food waste. In the UK alone, the cost of food waste amounts to £13.8 billion/annum. And besides economic loss, global food waste is accountable for around 8% of the total greenhouse gas emissions. This interdisciplinary Ph.D. research will therefore focus on building sustainable solutions to address food waste powered by Artificial Intelligence (AI) innovation, aiming to reduce and manage food waste by exploring their conversion into value-added products. The overall aim would be, to apply data-driven simulation(s), and establish an innovative food model.

The outcome of this project has tremendous potential to make a key impact in the attainment of the second SDG goal by developing technology-based solutions to improve urban health and for other sustainable causes. Besides, the cutting-edge interdisciplinary research, this studentship will provide the Ph.D. candidate with ample opportunities to build upon their career in science and technology. Like, dissemination of research work/outputs as well as networking in internal and/or external relevant conferences, scientific publications in peer-reviewed journals, innovation to commercialisation prospects (e.g., ICURe), etc.

Candidates with a background in Data Science (or other relevant areas) and a strong interest in applied biotechnology (or willingness to learn) are encouraged to apply for this position. For any informal queries please contact Dr Dipankar Sengupta ([Email Address Removed]).

Supervisory Team – Dr Dipankar Sengupta, Dr Pooja Basnett, Dr Linda Percy 


Funding Notes

Applications are invited for a Full Research Studentship which is tenable for up to three years for full-time study starting in January 2023. Overseas applicants are welcome though will have to pay the difference between the Home and Overseas fee rates. The students will be offered a stipend of £17,285 (fixed to UKRI Rate) per annum and £3000 per annum for consumables. Students will be funded full time for 3 years. Students will also be encouraged to assist with demonstrating practical classes and will be paid the rate for demonstrators.

References

1. Sengupta, D., & Ghosh,R. (2022). Artificial Intelligence for addressing smart cities poor urban health. In Second International Conference on Water, Megacities and Global Change, 11-14th January 2022, UNESCO Paris.
2. Grau, I., Sengupta, D., Garcia Lorenzo, M. M., & Nowe, A. (2020). An Interpretable Semi-supervised Classifier using Rough Sets for Amended Self-labeling. In Proceedings of the 2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) (pp. 1-8). IEEE.
3. Basnett, P., Marcello, E., Lukasiewicz, B., Panchal, B., Nigmatullin, R., Knowles, J.C. and Roy, I., (2018). Biosynthesis and characterization of a novel, biocompatible medium chain length polyhydroxyalkanoate by Pseudomonas mendocina CH50 using coconut oil as the carbon source. Journal of Materials Science: Materials in Medicine, 29(12), pp.1-11.
4. Lukasiewicz, B., Basnett, P., Nigmatullin, R., Matharu, R., Knowles, J.C. and Roy, I., (2018). Binary polyhydroxyalkanoate systems for soft tissue engineering. Acta Biomaterialia, 71, pp.225-234.
5. Basnett, P., Lukasiewicz, B., Marcello, E., Gura, H.K., Knowles, J.C. and Roy, I. (2017). Production of a novel medium chain length poly (3‐hydroxyalkanoate) using unprocessed biodiesel waste and its evaluation as a tissue engineering scaffold. Microbial biotechnology, 10(6), pp.1384-1399.

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Research output data provided by the Research Excellence Framework (REF)

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