Research Assistant
Opportunity ID: 11873
Faculty: Science & Engineering
Location: QM London
Contract Type: Fixed Term
Duration: 12 months
Working Patterns: Full Time
Full Time Equivalent (FTE): 1.00
Working Hours (Per Week): 35.00
Salary Range: £39,187-£39,802 per annum
Contact Details
Name: Dr M Hasan Shaheed
Email: m.h.shaheed@qmul.ac.uk
About the Role
The School of Engineering and Materials Science at Queen Mary University of London is looking for a Research Assistant (RA) to join the "Self-Learning AI Recipe Engine" project, carried out in collaboration with an agri-tech industry partner.
The project explores how AI, computer vision, and reinforcement learning can optimise growing conditions in indoor vertical farming. Under the guidance of Dr M Hasan Shaheed, the successful candidate will help develop and test AI models using crop images and sensor data.
The role will involve preparing and labelling datasets, supporting the training and evaluation of computer vision models for tasks such as plant growth monitoring, and assisting with reinforcement learning models that recommend growing conditions.
This position offers an opportunity to gain experience across AI, data analysis, computer vision, and controlled-environment agriculture within a collaborative research setting.
This is a full-time, 12-month position.
About You
We welcome applications from candidates with an MSc in Artificial Intelligence, Machine Learning, Computer Science, Data Science, or a closely related subject.
You should have some experience working with machine learning in Python, ideally using tools such as PyTorch or TensorFlow, and an interest in applying AI to real-world challenges. Experience with computer vision, image analysis, and preparing datasets for machine learning projects would be beneficial.
We are looking for someone who is comfortable working with data, evaluating model performance, and applying statistical techniques to analyse results. The candidate will also contribute to experimental trials at the industry partner's growing facility, help analyse results, and support the preparation of research publications.
Experience with reinforcement learning, sensor data, or agricultural applications of AI would be an advantage, but is not essential.
About the School
The School of Engineering and Materials Science (SEMS) is a large, research-intensive School within the Faculty of Science and Engineering, comprising over 110 academic staff and a vibrant community of approximately 2,700 undergraduate and taught postgraduate students, alongside more than 250 PhD researchers. The School offers a dynamic and interdisciplinary research culture, ranked 7th overall in the UK for research excellence in REF2021, and 2nd nationally for world-leading research outputs.
About Queen Mary
At Queen Mary University of London, we believe that a diversity of ideas helps us achieve the previously unthinkable.
Throughout our history, we’ve fostered social justice and improved lives through academic excellence. And we continue to live and breathe this spirit today, not because it’s simply ‘the right thing to do’ but for what it helps us achieve and the intellectual brilliance it delivers.
We continue to embrace diversity of thought and opinion in everything we do, in the belief that when views collide, disciplines interact, and perspectives intersect, truly original thought takes form.
Benefits
We offer competitive salaries, access to a generous pension scheme, 30 days’ leave per annum (pro-rata for part-time/fixed-term), a season ticket loan scheme and access to a comprehensive range of personal and professional development opportunities. In addition, we offer a range of work life balance and family friendly, inclusive employment policies, flexible working arrangements, and campus facilities.
Queen Mary’s commitment to our diverse and inclusive community is embedded in our appointments processes. Reasonable adjustments will be made at each stage of the recruitment process for any candidate with a disability. We are open to considering applications from candidates wishing to work flexibly.
Closing Date: 20/11/2026, 23:55
