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BBSRC Food Consortium CTP PhD Studentship - Artificial Intelligence and Machine Learning Systems to Predict Food Product Shelf-life

University of Reading

Qualification Type: PhD
Location: Reading
Funding for: UK Students
Funding amount: £17,668 per year plus a £1540 enhancement from the industrial sponsor), support for tuition fees at the standard UK rate (currently £4,596 per year) and a contribution towards research costs (4 years award)
Hours: Full Time
Placed On: 1st December 2022
Closes: 31st January 2023
Reference: GS22-053
 

The Food Consortium CTP is looking for a PhD candidate for the BBSRC funded project “Artificial Intelligence and Machine Learning systems to predict food product shelf-life. The PhD will be completed in the University of Reading under the supervision of Professor Keshavan Niranjan (University of Reading) and Dr Francesca Giuffrida (Societes des Produits Nestlé).

The Food Consortium CTP comprises four major food manufacturers together with the largest UK-based independent science and technology provider and trainer for the food industry (Campden BRI), and the Haydn Green Institute (Nottingham University Business School).

This industry-led collaborative programme will develop highly skilled PhD researchers and provide an innovation ecosystem through collaboration and partnership. As a successful PhD candidate, you will be part of a larger cohort of students with the opportunity to form strong links to industry and be part of a supportive network of peers, academic supervisors, industrial supervisors, and training partners.

Business facing training will include concepts and issues to consider when commercialising early-stage science and technology, using tools to help evaluate innovation and commercialisation strategies.

About the Project

Lipids are a major constituent of foods which are worth around USD 50 Billion. Unsaturated lipid components [e.g. linoleic, arachidonic, eicosapentaenoic and docosahexaenoic acids] undergo oxidation which severely impacts on food quality and shelf-stability through flavour and taste deterioration and decreases in nutritive value. This project hypothesises that Artificial Intelligence and Machine Learning allow prediction of quality changes occurring in a product, or to formulate new food products which are stable. Such models result in a substantial reduction in food waste, time and resources.

The student will gain considerable training in experimental and mathematical modelling methods used in shelf-life and keeping quality assessment at the University of Reading. In addition, the student will be trained in the use of latest Machine Learning and Artificial Intelligence methods and apply these to food systems – which is still an area in its infancy.

Nestlé Research Center based in Lausanne, Switzerland, will host the student giving the opportunity to be trained on the use of the state-of-the-art analytical techniques.

The student will also be given the opportunity to do a range of short courses at Reading University and complementary Soft Skills courses.

Entry Requirements

Candidates are expected to hold (or be about to obtain) a minimum upper second-class honours degree (or equivalent) in a related area/subject.  Equality, diversity and inclusion is fundamental to the success of the programme.  The full equality, diversity and inclusion plan for the Food Consortium is available on request.

Due to restrictions on the funding, this opportunity is only open to candidates who qualify as a Home student, as set out by the UKRI at View Website. The funding will include a tax free stipend (currently minimum £17,668 per year plus a £1540 enhancement from the industrial sponsor), support for tuition fees at the standard UK rate (currently £4,596 per year) and a contribution towards research costs (4 years award).

How To Apply

To apply, please submit an online application for a PhD in Food and Nutritional Sciences at https://www.risisweb.reading.ac.uk/si/sits.urd/run/siw_ipp_lgn.login?process=siw_ipp_app&code1=P_ADM&code2=0001&_ga=2.75077650.1495320261.1583744978-1661293698.1578304433 quoting reference GS22-053 in the ‘studentships applied for’ section.

Deadline for applications – 31 January 2023

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