Dr Iestyn Stead

Supervisor Details

Contact Details

Department of Mechanical Engineering, University of Birmingham

Scientific Background

Research Interests

Dr Stead is a tribologist specialising in condition monitoring of complex mechanical systems. This fundamental work enables the development of disruptive technologies including novel lubricants and bioremediation technologies to target treating and removing persistent and mobile chemical pollution. With a fundamental understanding of friction, wear, and materials he employs acoustic emissions, vibration, force, and imaging to infer insights into the condition of complex systems. This has provided information into the condition of lubricated contacts, machining processes, phenotyping for toxicology and the health of bioremediation technologies.

Research Groups

Daphnia Facility

Supervision Style

In three words or phrases: Goal driven, collaborative, guiding.

Provision of Training

Training will be provided in collaboration with the research team, consisting of my PhD students, post docs, facility technicians and the supervisory team.

Progression Monitoring and Management

I will set out my expectations of your progression on a weekly/monthly basis depending on the goals at the time. If these progression criteria are not met we will always discuss a plan of action together.

Communication

The team has a WhatsApp group chat with which we regularly communicate during the week and very occasionally on the weekends if needs arise. Whilst I expect you to keep up communications the it is the responsibility of the whole team to respect other's communication preferences and a positive work/life balance is actively promoted. This is also supported through weekly team meetings. I am happy to discuss any issues that are impacting your ability to fulfil your potential or my/our expectations.

Meetings

PhD Students can expect scheduled meetings with me at least once per week. These meetings will be face-to-face and I am usually contactable for an instant response (if required) on every working day.

Work Patterns

The timing of work in my lab is completely flexible, and (other than attending pre-arranged meetings) I expect students to manage their own time.

Notice Period for Feedback

I need at least 2 weeks’ notice to provide feedback on written work of up to 5000 words.

MIBTP Project Details

Primary supervisor for:

What's Going On? AI-Driven behavioural phenomics and multiomics revealing evolutionary responses to mixtures in resurrected Daphnia magna

See the PhD Opportunities section to see if this project is currently open for applications via MIBTP.

Please Note: The main page lists projects via BBSRC Research Theme(s) quoted and then relevant Topic(s).

What's Going On? AI-Driven behavioural phenomics and multiomics revealing evolutionary responses to mixtures in resurrected Daphnia magna

Secondary Supervisor(s): Prof Luisa Orsini

University of Registration: University of Birmingham

BBSRC Research Themes:

Understanding the Rules of Life (Systems Biology)

Project Outline

Persistent and mobile chemicals, including perfluoroalkyl and polyfluoroalkyl substances (PFAS), are an increasing threat to freshwater ecosystems because they persist in the environment, move easily through water, and accumulate in food webs. Understanding how species respond and adapt to these pollutants is essential for predicting and managing their ecological impacts.

This project will investigate evolutionary responses to chemical pollution using resurrected and contemporary populations of the freshwater crustacean Daphnia magna. Resurrected populations recovered from sediment archives provide a unique opportunity to compare historical and modern responses to pollution across evolutionary timescales. Using artificial intelligence and machine vision based on YOLO, the project will automatically analyse behavioural responses of Daphnia exposed to PFAS and realistic chemical mixtures. High-throughput video analysis will quantify traits including swimming behaviour, heart rate, and stress responses, providing sensitive indicators of toxicity.

Behavioural data and omic data from mixtures will be integrated with transcriptomic and metabolomic datasets from the Horizon project PrecisionTox to identify the molecular pathways associated with pollutant detection, tolerance, accumulation, and detoxification. Statistical modelling will be used to identify phenotypic and molecular signatures linked to resilience or susceptibility to chemical exposure. The project will provide new insights into how freshwater organisms adapt to emerging pollutants and will contribute to the development of more sensitive approaches for environmental monitoring. In the longer term, the findings will support future functional genomics research and the development of enhanced biological systems for detecting and managing persistent chemical pollution.

Co-supervisor on a project / projects with