Research Associate in Development of a Machine Learning based Scoring Method for Renewable Energy Projects

A six month, fixed term appointment is available in the School of Engineering at the University of Edinburgh to work on the REPSCORE Innovate UK-funded collaborative project.

The objective of the project is to develop an automated scoring method based on machine learning methods to assess the performance of renewable energy projects and to assess the investment risk. This work is part of the REPSCORE project which is led by Enian and includes the Schools of Mathematics and Engineering at the University of Edinburgh. The work at the University of Edinburgh will be supervised by Dr Richtarik and Dr Friedrich.

The successful candidate will be responsible for all aspects of the project associated with the develop an automatic process to transform the labour-intensive manual scoring process into a scalable, automated process. This includes the development of algorithmic and machine learning techniques to transform the metric inputs of the current scoring model into one or a series of single outputs in the form of a score, the evaluation of the reliability of this score and the investigation of machine learning approaches to automate the inclusion of future data.

The successful candidate for this position should have a PhD in a numeric discipline and have extensive experience in algorithm development and data science. Experience in renewable energy projects and machine learning is highly desirable. The post is expected to start in October/November 2018. Candidates invited for interview will be informed no later than two working weeks after the close of applications.

Informal Enquiries to: Dr Daniel Friedrich (D.Friedrich@ed.ac.uk).

UoE Vacancy Ref: 

045165

Closing Date: 

Wednesday, October 3, 2018

Contact Name: 

Contact Email: 

Contact Telephone: 

+44(0)131 650 5662