Open Positions

Open positions, when available, will be announced below. However, if you are a highly motivated and capable student interested in gaining research experience and co-authoring high-quality publications, you are welcome to get in touch. We occasionally offer voluntary research internships, which are not salaried but provide valuable hands-on experience. To express your interest, please email your CV to oktay.cetinkaya@newcastle.ac.uk. 

A funded internship position is available (NCL STUDENTS ONLY)! -Closing on 23rd November 2025

I am offering an exciting internship opportunity titled "Mind the Gap!: Reconstructing Missing Water Meter Data with Machine Learning" for one student at Stage 2 or above (including PGRs), studying Electrical and Electronics Engineering, Computer Science, or a related discipline at the School of Engineering, Newcastle University.

This internship is expected to be for a total of 60 hours. You will work up to 5 hours per week between March and May 2026. Working days and times are flexible, and the work will take place on campus, likely on Merz Court (ISC Research Group PhD offices or the Sensors Laboratory).

To apply, please visit https://mycareer.ncl.ac.uk/user/app/jobs/view/ZXO47 

About the role

In the UK, ageing water pipelines lose over three billion litres of treated water every day due to leaks that are hard to detect. Replacing entire networks is neither practical nor affordable, so water companies are turning to digital monitoring. Smart meters, combined with flow and pressure sensors, are now being deployed to track hourly consumption data, which can be interrogated to spot anomalies that may pinpoint leaks or bursts. However, these meters face a major challenge: data gaps. The UK Water Services Regulation Authority (Ofwat) requires an 85% hourly data success rate (DSR), yet current wireless technologies such as LoRaWAN and NB-IoT often deliver only 60-70%, even in good signal conditions. Closing this gap by adding more gateways, i.e., densifying the network to minimise packet losses, would cost water companies millions, involving also lengthy planning permissions and public reluctance. This project aims to address this gap using data science and machine learning to reconstruct missing water meter data, improving network reliability and leak detection without costly infrastructure expansion.

Working with a large dataset provided by Connexin Ltd, as an intern, you will analyse millions of historical meter transmissions to identify patterns in household consumption and develop models to reconstruct missing readings. Methods such as recurrent neural networks (RNNs), long short-term memory (LSTM) networks, or other modern architectures (e.g. Transformer-based or GANs) may be explored, depending on progress. You will be closely supervised by Dr Oktay Cetinkaya (School of Engineering) and supported by Mr Jarod Gillespie (Head of Strategy and Delivery, Connexin), ensuring you receive ongoing guidance and feedback throughout the project. This role provides hands-on experience at the intersection of IoT, machine learning, and sustainability, developing practical skills in data analysis and model validation while contributing to one of the UK’s key infrastructure challenges.

Key tasks

• Preprocessing and cleaning raw smart-meter data to locate gaps (Week 1-2)

• Training predictive models that learn from complete sequences of data (Week 3-4)

• Testing model accuracy by deliberately removing known readings and checking how well the model predicts them, using measures like RMSE and MAE (Week 5)

• Benchmarking against simple alternatives, such as linear interpolation (Week 5)

• Compiling results, preparing the final report, and conducting the industrial supervisor feedback session (Week 6)

Essential skills

• Basic experience of Python or MATLAB

• An interest in data analysis, IoT, distributed sensing, and machine learning/AI

Desirable skills

• Critical thinking and problem-solving

• Creativity and innovation in approaching challenges

• Strong communication and presentation skills

• Time management and organisational ability

• Willingness to learn new tools and methods

Degree discipline

Students studying Electrical and Electronics Engineering, Computer Science, or a related discipline are welcome to apply.

To be eligible to apply, you must be a Newcastle University undergraduate/postgraduate student registered on a programme of study throughout the 2025-26 academic year. For full details of eligibility, see the Working on Campus website.

Work done as part of this role can count towards an NCL+ Award, which can help you reflect on the skills you have gained and how to use them when applying for jobs in the future; to find out more, visit our webpages. 

Closed Positions

A fully-funded PhD position is available (UK HOME STUDENTS ONLY)!

I am excited to announce a fully-funded PhD opportunity under the prestigious EPSRC Doctoral Landscape Awards (DLA). This position provides an exciting opportunity to engage in cutting-edge research at the intersection of ML/AI, drone technology, and ground-penetrating radars (GPRs) to address the critical challenge of leak-fueled water losses in distribution networks.

Application details can be found here by searching the project title. For any inquiries, feel free to contact me at oktay.cetinkaya@newcastle.ac.uk.

The full project proposal is available below:

Research Assistant/Associate in Sensor Systems

I'M HIRING! Join me at Newcastle University to develop networked sensor systems, powered by ML algorithms, to monitor and assess food freshness, helping reduce household food waste.

Fixed-term: 9 months.

Deadline: 12th January 2025.

Details: https://www.linkedin.com/jobs/view/4104067358/