Postdoctoral Researcher in Machine Learning and Power Systems
Salary
Grade 8: £48,235 - £57,255
Closing date
Feb 19, 2025 12:00PM
Description
We are seeking a full-time Postdoctoral Research Assistant in Machine Learning and Power Systems to join the Energy and Power Group at the Department of Engineering Science (Osney). The post is funded by the National Energy System Operator and is fixed-term to 19 December 2025.
Reporting to the Principal Investigator, Professor Malcolm McCulloch, the post holder will join the Real Time Predictor Innovation project. The main objective of this project is addressing the critical need to predict accurate minute-by-minute frequency- aware electricity load prediction aligning with NESO's unique operational requirements. You will be responsible for
• Developing pre-processing methods (statistical-based method, machine-learning based method, proposed hybrid method) and comparing them.
• Adapt existing machine-learning methods and develop hybrid ones for high-resolution load forecasting (based in Python).
• Define different scenarios and clusters based on different features.
• Visualizing the results
• Analyse data and field research towards academic publications.
• Support monitoring and evaluation of the project.
• Produce the required reports associated with the project objectives.
• Tasks as required by the funder and/or in order to publish high-quality journal papers.
You should possess a relevant Ph.D/D.Phil with post-qualification research experience, possess specialist knowledge in the analysis and prediction of time-series data using both traditional and machine learning techniques, plus is proficient in Python and Machine Learning programmes. There is the possibility to underfill at Grade 7 (£36,024- £44,263p.a.) if the candidate holds a relevant PhD/DPhil or is near completion (please note that ‘near completion’ means that you must have submitted your thesis) and has the relevant experience.
Informal enquiries may be addressed to Gemma Watson (email: gemma.watson@eng.ox.ac.uk)
For more information about working at the Department, see
www.eng.ox.ac.uk/about/work-with-us/
Only online applications received before midday on 19 February 2025 can be considered. You will be required to upload a covering letter/supporting statement, including a brief statement of research interests (describing how past experience and future plans fit with the advertised position), CV and the details of two referees as part of your online application. Interviews are expected to be held on 26 February 2025.
The Department holds an Athena Swan Bronze award, highlighting its commitment to promoting women in Science, Engineering and Technology.
Contact name
Gemma Watson
Contact email
gemma.watson@eng.ox.ac.uk