Research Assistant/Fellow in Machine Learning for Advanced Sensor Monitoring

Cranfield, United Kingdom
£30,600 (Research Assistant) or £33,309 (Research Fellow)
23 Jul 2020
End of advertisement period
23 Aug 2020
Contract Type
Fixed Term
Full Time

School/Department School of Water, Energy and Environment
Based at Cranfield Campus, Cranfield, Bedfordshire
Hours of work 37 hours per week, normally worked Monday to Friday. Flexible working will be considered
Contract type Fixed term contract
Fixed Term Period 12 months
Salary £30,600 (Research Assistant) or £33,309 (Research Fellow)
Apply by 23/08/2020

Role Description

Research Assistant (if PhD near to completion) or Research Fellow (if PhD completed) in Machine Learning for Advanced Sensor Monitoring

Cranfield University, Centre for Renewable Energy Systems at the Energy and Power Theme, welcomes applications for the position in Machine Learning for Advanced Sensor Monitoring.

As the UK’s only exclusively postgraduate university, Cranfield’s world-class expertise, large-scale facilities and unrivalled industry partnerships is creating leaders in technology and management globally.  Our distinctive expertise is in our deep understanding of technology and management and how these work together to benefit the world.

Our people are our most valuable resource and everyone has a role to play in shaping the future of our university, developing our learners, and transforming the businesses we work with. Learn more about Cranfield and our unique impact here: Working life at Cranfield.

Our shared, stated values help to define who we are and underpin everything we do: Ambition; Impact; Respect; and Community. To find out more please visit our website:

Centre for Renewable Energy Systems (CRES) has been awarded an Innovate UK project ‘Development of a shortlist-and-test diagnostic platform for brucellosis in livestock (ref:104989)’. This is a UK-China collaborative project that aims to develop an advanced IoT platform for screening and diagnosis of diseases in order to improve animal welfare and agricultural productivity. Information of the project can be found on the Cranfield website and Innovate UK website In the project the RF/RA is required to evaluate the sensor data provided by the company IceRobotics, including developing the training set of data, developing the model and algorithms, data validation and testing. While the project particularly focuses on brucellosis and other common diseases including mastitis, metritis and BVD, the development in machine learning of data analysis in this area can also extend to broader disease detection, including human diseases. Our research team also have other ongoing research projects on wearable sensors for applications like Parkinson’s disease, foetal ECG and post-stroke rehabilitation, where their research opportunities can be further explored in this position.

You will hold a PhD (or near completion) in Machine Learning, Data Science, Artificial Intelligence or related discipline. You will also be involved in collaboration with academic and industrial partners. The ability to communicate, rapidly assess information, resolve problems and respond in a timely manner is essential. You will have experience in machine learning, artificial intelligence, sensors and data analysis, together with a developing track record of publishing in high quality publications. Industrial experience would be an advantage.

In return, you will have exciting opportunities for career development in this key position, and to support world leading research and education, joining a supportive team and environment.

At Cranfield we value Diversity and Inclusion, and aim to create and maintain a culture in which everyone can work and study together harmoniously with dignity and respect and realise their full potential.  To further demonstrate our commitment to progressing gender diversity in STEM, we are members of WES & Working Families, and sponsors of International Women in Engineering Day.

Our equal opportunities and diversity monitoring has shown that that women and minority ethnic groups are currently underrepresented within the university and so we actively encourage applications from eligible candidates from these groups.

We actively consider flexible working options such as part-time, compressed or flexible hours and/or an element of homeworking, and commit to exploring the possibilities for each role.  To find out more, please visit

For an informal discussion, please contact Dr Jerry Luo, Lecturer in Energy Storage and Harvesting, via email on (E);

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