Research Assistant/Associate - Second-life Battery Storage for Net Zero Grids

Research Assistant/Associate - Second-life Battery Storage for Net Zero Grids

Requisition ID: 28099

Location: Newcastle, GB

Contract Type: Fixed Term

Working Pattern: Full Time

Posted Date: 17 Apr 2025

Research Associate: £35,116 per annum

Closing Date: 01 May 2025

The role

This is an excellent opportunity to join a leading team in Electrical and Electronics Engineering, School of Engineering, Newcastle University.

The EPSRC Northern Net Zero Accelerator (NNZA) project aims for the adoption of unsupervised learning techniques to cluster second-life EV batteries for PV solar and wind energy storage systems.

The interdisciplinary idea translation is based on existing knowledge within Newcastle University from two research areas: Intelligent Sensing and Net-zero Electrification. The knowledge/expertise on in-line testing and characterization of batteries from projects with the EVOEMs (Nissan, Renault, Daimler) will be leveraged. Additionally, knowledge on data-driven decision making from The ReLiB Project (Faraday Institution's flagship, £18m, research project on challenges of re-using and recycling lithium-ion batteries) will also be applied. The knowledge/expertise on Intelligent Multimodal Sensing achieved from UKRI/EPSRC/Innovate-UK/Dstl funded projects will be leveraged for this work, including unsupervised learning, data clustering, statistical modelling, noise and heterogeneous information fusion, and multiple-source localization and tracking.

You should be educated to at least MSc (or equivalent degree qualification) in EEE (PhD award essential for Research Associate). You should have a demonstrable record of signal processing/machine learning algorithms.

This position is available immediately on a full-time, fixed-term basis for 6 months from the contract start date.

Key Responsibilities

• Work with Dr. Mohsen Naqvi (PI) in the Intelligent Sensing and Communications Research Group, Dr. Simon Lambert (CoI), and Dr. Mazhar Abbas (CoI) in the Electrical Power Research Group, within the School of Engineering.
• Learn to use EC-lab software available in the Electrical Power Testing (EPT) Lab as the second-life batteries' characterization data is archived as EC-lab files.
• Process retired batteries' data. The second-life batteries and sources to get data are available under existing project agreements.
• Apply unsupervised learning techniques to screen batteries based on preprocessing and features/parameters extraction.
• Design a distributed control system based on clusters, metadata, and energy generation (PV, Wind) processes.
• Utilize the unique multi-sensor research lab facility, where multimodal sensors operate in parallel, completely time-synchronized, specifically with overlapping fields of view.

The Person (Essential)

Knowledge, Skills and Experience

• Track record of design, deployment, and evaluation of machine/deep learning-based techniques.
• Experience working on multi-sensor data.
• Excellent communication skills, including the ability to present detailed or technical information and collaborate with others to understand technical and user requirements.
• Basic knowledge of programming languages (e.g., Python, Matlab, etc.).
• Evidence of originality, innovation, creativity, and research track record, including publications in leading conferences in Signal Processing and high-caliber journals.
• Experience demonstrating to undergraduate and PGT students.

Attributes and Behaviour

• Ability to work on own initiative with demonstrable problem-solving skills.
• Ability to complete tasks independently and flexibly at the discretion of the Principal Investigator and Team.
• Ability to work as a member of dedicated research groups, in a collaborative manner with team colleagues at all levels.
• Commitment to embedding the values of equality and diversity in all aspects of work.
• Demonstrated interpersonal skills with the ability to interact and collaborate with people from industry.

Qualifications

• PhD (awarded or close to submission) in EEE or a closely related discipline. (Essential for Research Associate)
• MSc (or equivalent) in Signal Processing or Machine Learning Subjects. (Essential for Research Assistant / Desirable for Research Associate)

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