Job Description

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Engineering Department

Location:

Newport, Shropshire TF10 8NB

Salary:

35,166 per annum

Post Type:

Full Time

Contract Type:

Fixed Term - Until 30 September 2028

Closing Date:

23.59 hours BST on Friday 15 August 2025

Reference:

IM-1930


Position: Doctoral Candidate #5 (DC 5)


Project: Safe and legal operation of robots in agricultural environments


Host Institution: Harper Adams University - United Kingdom


PhD programme: Harper Adams University PhD programme


Fixed term until 30 September 2028

Research project description

Agricultural robots and Artificial Intelligence (AI) technologies could soon be helping farmers improve global food security by alleviating labour shortages, increasing efficiency, sustainability and resilience to climate change, and reducing the use of chemicals, fertiliser, water and energy; thereby minimising farming's environmental impact.


AI machine learning offers a new expedient method of developing control systems for tasks that would be difficult to manage using classical technologies. Agricultural applications present a unique opportunity for AI systems as they often involve repeatable tasks within a relatively low-safety-risk environment, unlike public or transportation applications.


Although some agricultural technology developers are already incorporating AI systems in their products to enhance the control and functionality, there are growing concerns about functional safety regulations and product certification due to the inherent uncertainty of how AI systems make decisions. Classical engineering development guidelines, are difficult to interpret or simply not transferrable to AI systems. There are virtually no satisfactory ways of exhaustively ensuring and demonstrating that these stochastic systems meet the demonstrable, repeatable, and predictable expectations of existing safety legislation. This is hindering their development and delaying their introduction into the market.


The engineering process for ensuring compliance with functional safety requirements involves thorough risk analyses for the intended application and product. This includes evaluating the system's ability to perform reliably under environmental and technical limitations, and establishing redundancies, where necessary, to ensure these limits are maintained and honoured. Tools like FMEA and HARA are staples in this process which typically involves the establishment of manageable boundaries within which the system is intended to function and demonstration of the system's safe operation within these regions, usually requiring the system to abort, or go to a default safe state, when the intended conditions are not met.


For example, lane keep assist on automotive vehicles (whereby the vehicle ADAS system attempts to keep the vehicle within road markings through small corrective steering inputs) will immediately abort (or not engage) if the bright white lines that fit a defined and rigid expectation are not clearly visible. These systems use algorithms, rather than AI machine learning, to detect road markings and, if the system does not detect well understood, highly demonstrable and (critically) highly repeatable parameters for lane markings, it gives a null result and does not attempt any corrective steering.


A non-deterministic AI machine learning model for the identical task would not offer this demonstrability or, critically, the repeatability of classical algorithm-based systems. Furthermore, there is no guarantee that a concurrent redundant model will return an identical result for basic cross-checking. To overcome these issues AI system developers often resort to running parallel classical-based systems to act as an oversight of the AI. This is costly, complex, and time consuming, nullifying the benefits of using an AI approach.


This project's two aims are (1) Establish the best approach for developing machine learning based control systems for agricultural applications that will allow developers to demonstrate that their AI systems meet safety requirements, based on reviewing and interpreting current legislation for non-deterministic AI systems. (2) Create new compliance testing procedures and processes for agricultural AI machine learning systems. These are essential for manufacturers developing these systems, and will accelerating the supply of AI machine learning controlled machinery to farmers unlocking all of the benefits described in the first paragraph.


Objective: Achieve both of the above.


Expected Result: Achieve both of the above.

Secondments

The secondments planned for this research project are at:


Any European University or Company justified by the candidate that is willing to take them.

Desirable skills, qualifications and specific requirements

Your application should, ideally, respect the AIGreenBots general requirements and eligibility criteria as described here https://aigreenbots.eu/recruitment/general-info 


Have a valid European Master's degree, or equivalent, in law, manufacturing and/or engineering.


Be fluent in legal and technical English and at least one other European language.


Motivation, flexibility, sense of responsibility, ability to listen and compromise, autonomy, and problem-solving skills.


Ability to work with a small cutting edge commercial/academic technology team, and work with a high level of autonomy and integrity when unsupervised.


Candidates should be prepared and able to travel internationally, often at short notice, this is not negotiable.


It is also highly desirable that you have a sound understanding of robotics, machine learning, and AI.

Benefits

Attractive salary up to 35,116 per annum


Excellent conditions including - social security tax, health costs, PhD tuition fee, mobility allowance, family allowance (if eligible)


Mobility allowance (if applicable): 600EUR/month


Family allowance (if applicable): 495EUR/month


Research, training and networking costs covered: Registration and attendance at international conferences.


Fixed term until 30 September 2028

How to apply

Please and submit full Curriculum Vitae (to include two referee details) and supporting documents by no later than midnight on 15 August 2025


Should you require any adjustments to complete your application for this role then please contact vacancies@harper-adams.ac.uk

Additional information

Supervisors of this PhD project: Prof. Fernando Alfredo Auat Cheein, Dr Richard CP Green


Host institution and living conditions: Harper Adams University is a specialist agriculture, agribusiness, engineering, food and rural university based central England, and the University currently has more than 5,000 students enrolled. Harper Adams University has regularly been the top specialist UK university of the year. Undergraduate and postgraduate programmes are available in agriculture and crops, animal studies, business, management and marketing, countryside, environment and wildlife, engineering, food studies, geography, rural estate, property and land management and veterinary studies. Harper Adams University performs very well in a number of national rankings. Its graduate employment rate is currently >98%, student satisfaction is 89% and it is in the top five most safe and most welcoming UK universities. HAU will be responsible for WP7 Safe and legal operation of robots in agricultural environments. This will involve the delivery of a workshop for all the Researchers. HAU will also be responsible for supervising DC5 and will host other Researchers providing them real-world experiences in the HAU's Hands Free Robotic Farm.


A requisite to apply is to have a valid European Master's degree, or equivalent, in law, manufacturing and/or engineering and meet the University's entry requirements for doctoral study.


At this stage we have no concept of what the ideal candidate looks like, we encourage applications from anyone regardless of their sex, religion, ethnicity, physical ability, or sexuality that feels they meet the brief and are up to the challenge.