The Role
We are seeking a motivated and talented Research Associate to join the Northern Net Zero Impact Accelerator project titled ECO-HAVEN: Energy Conservation for Households using Adaptive Edge Learning. This project develops a low-cost, privacy-preserving embedded AI device to reduce household carbon emissions through smart appliance energy profiling and explainable feedback mechanisms.
The successful candidate will play a key role in delivering a hardware-software prototype for Non-Intrusive Load Monitoring (NILM) that can provide real-time, interpretable energy-saving suggestions to households—completely on-device. This role involves applied machine learning research, hardware interfacing, and community-facing development through focus groups and user feedback.
The position is full time (37 hours per week) and is fixed-term for 6 months and will be based in the School of Engineering.
You will work under the guidance of Tousif Rahman and Prof. Rishad Shafik.
For informal queries please contact Dr. Tousif Rahman at:
To apply, please provide evidence of how you meet the essential criteria required for the role outlined in ‘The Person’ by uploading a letter of application along with your Curriculum Vitae (CV).
For more information on the school of engineering, please click
As part of our commitment to career development for research colleagues, the University has developed 3 levels of . These profiles set out firstly the generic competences and responsibilities expected of role holders at each level and secondly the general qualifications and experiences needed for entry at a particular level.
Key Accountabilities
• Design and develop embedded AI algorithms for appliance profiling using smart meter data
• Benchmark performance against state-of-the-art NILM approaches using datasets like REFIT and UK-DALE
• Implement real-time data streaming simulations for adaptive training and testing
• Develop an explainability framework for real-time user feedback
• Collaborate with project partners to evaluate performance and real-world applicability
• Engage with households via focus groups and questionnaires to co-design feedback interfaces
• Document project outputs for internal reports, publications, and follow-on funding proposals
The Person
• Experience with embedded systems (e.g., Raspberry Pi, ARM Cortex, microcontrollers)
• Strong programming skills in Python, Verilog/VHDL and C/C++
• Understanding of machine learning frameworks (e.g., Scikit-learn, TensorFlow Lite)
• Demonstrated interest or experience in energy systems, NILM, or edge AI
• Experience in developing or evaluating explainable AI or interpretable ML systems
• Ability to work independently and meet deadlines
• A publication record or demonstration of technical dissemination activities
Desirable
• Experience working with real-time streaming data or event-driven systems
• Familiarity with hardware prototyping (e.g., sensor interfacing, PCB integration)
• Knowledge of privacy-preserving learning methods
• Experience working with diverse user groups and participatory design methods
Attributes and Behaviours
• Excellent communication skills, both written and verbal
• Ability to liaise with industrial partners and community stakeholders
• Strong interpersonal skills and the ability to work within an interdisciplinary team
• A proactive and reflective approach to development and problem-solving
Qualifications
Research Assistant post
● Nearing completion or working towards a PhD in Electronic Engineering, Machine Learning with a hardware focus or related area. Candidates without PhD will still be considered under exceptional circumstances only if they can demonstrate an extremely good fit across both essential and desirable criteria below
Desirable for Research Assistant post
● Relevant skills and experience that this particular project requires. This would be based on hands-on experience with Tsetlin Machine algorithms and their hardware implementation and publications indicative of being capable of working at a PhD level
Research Associate post
● A fully completed and awarded PhD in Electronic Engineering, Machine Learning with a hardware focus or related area.
Newcastle University is a global University where everyone is treated with dignity and respect. As a University of Sanctuary, we aim to provide a welcoming place of safety for all, offering opportunities to people fleeing violence and persecution.
We are committed to being a fully inclusive university which actively recruits, supports and retains colleagues from all sectors of society. We value diversity as well as celebrate, support and thrive on the contributions of all of our employees and the communities they represent. We are proud to be an equal opportunities employer and encourage applications from individuals who can complement our existing teams, we believe that success is built on having teams whose backgrounds and experiences reflect the diversity of our university and student population.
At Newcastle University we hold a silver award in recognition of our good employment practices for the advancement of gender equality. We also hold a Bronze award in recognition of our work towards tackling race inequality in higher education REC. We are a employer and will offer an interview to disabled applicants who meet the essential criteria for the role as part of the offer and interview scheme.
In addition, we are a member of the Euraxess initiative supporting researchers in Europe.
Requisition ID: 28237
