Associate Professor, Faculty of Engineering
Nik is a Chartered Engineer with a MEng in Mechanical Engineering (University of Hull, 2006) and PhD in Chemical Engineering (University of Leeds, 2010). Nik Joined the University of Nottingham in 2014 and is an Associate Professor of Chemical Engineering. Since joining the University of Nottingham Nik has published over 30 journal articles and led projects funded by Innovate UK, EPSRC, STFC and the Royal Academy of Engineering.
Nik is a member of the Food, Water, Waste (FWW) Research Group and associate member of the Advanced Manufacturing Technologies (AMT) Research Group.
·CHEE4061: Food Processing
Nik's research is focussed on data-driven in-process sensing to deliver sustainable, safe and productive food manufacturing systems. Data-driven sensing combines cost-effective in-process sensors… read more
Airborne Ultrasound for Real-time Measurement of Physical Structures
3 Year PhD fully funded studentship for Home EU students.
The world is full of multi-component products such as foods (biscuits and fruits) and automotive components (Hybrid materials). The physical structure of these products affects their performance and there is a need for methods to characterise them during manufacturing. This project will utilise a novel airborne ultrasound instrument to develop techniques to characterise these structures. Airborne ultrasound systems use high frequency sound waves such as those used for medical imaging and non-destructive testing. However, they do not require direct contact with the sample under inspection, making them suitable for industrial environments. The project will focus on how the sound propagates through these systems and how data is recorded by the instrument. State of the art machine learning algorithms will also be developed to characterise different industrially relevant multi-component structures.
Requirements of studentship:
1) Students should have, or expect to obtain, a first-class or good 2:1 honours degree, or a distinction or high merit at MSc level (or international equivalent) in Engineering, Physics, Maths or Computer Science.
2) Students should be able to demonstrate an interest in developing instruments and measurement techniques. Students with experience or a willingness to learn software such as LabVIEW and MATLAB are encouraged to apply.
Nik's research is focussed on data-driven in-process sensing to deliver sustainable, safe and productive food manufacturing systems. Data-driven sensing combines cost-effective in-process sensors (e.g. optical and ultrasonic) with machine learning techniques and overcomes many of the challenges associated with utilising sensors to produce actionable information within manufacturing environments. Current areas of research include multi-sensor data fusion and emerging machine learning techniques such as transfer and distributed learning.
Nik's team has developed data-driven in-process sensing methods for a variety of applications including:
Nik is an active member of the UK's Digital Manufacturing research community and currently a Co-Investigator on the EPSRC's Digital Manufacturing Network: Connected Everything. Nik regularly speaks at Industry events on the topic of Digital Manufacturing, Industry 4.0 and Artificial Intelligence within the food and drink sector with invited international talks including: The Food and Drug Administration's Applications of Artificial Intelligence in Food and Cosmetics Safety Colloquium (2020) and the Australian Institute of Food Science and Technology Virtual Convention (2020). Nik has extensive industry collaborative experience with manufacturers in the food and drink, pharmaceutical and FMCG sectors ranging from micro SMEs to multinationals in addition to technology providers and integrators. Nik is currently a member on the EPSRC's Early Career Forum in Manufacturing Research and on the Food Standards Agency's Register of Experts.
I welcome enquiries from potential PhD candidates from Home, EU and international countries who are interested in the following research areas: Digital Food and Drink Manufacturing, Machine Learning, Sensor Fusion, Food Safety and Quality, Sustainable Food Systems.
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