Research Fellow in Advanced Machine Learning for Energy Systems

We are looking for a Research Fellow to join our NTU-GridLab team to work on advanced machine learning techniques in the area of energy systems with a specific focus on local energy communities encompassing storage systems (mobile and stationary). As a member of our team, you will be responsible for developing innovative algorithms and techniques at the intersection of advanced machine learning techniques and their application to the integration of energy systems and energy forecasting. This position requires expertise in advanced machine learning algorithms, advanced reinforcement learning, collaborative machine learning, and data-driven predictive modeling, analysis, and decision making. We are seeking candidates with a degree in computer science, electrical or mechanical engineering, applied mathematics, economics, or a related engineering-oriented discipline, as well as proven research experience and interests aligned with the aforementioned fields. You must be proficient in programming languages such as Python and have experience with relevant libraries and frameworks like TensorFlow and PyTorch. Furthermore, the candidate should demonstrate expertise in the application of machine learning techniques to energy systems through publications or projects. Applicants should possess a passion for research and knowledge transfer, possess a practical mindset for developing innovative solutions, work effectively independently and collaboratively, demonstrate analytical and conceptual thinking skills, and have a proficient command of English. This is an exciting externally funded position within NTU-GridLab.

For more details, please take a look at the attached Job Description & Person Specification . 

We'll still consider applications even if you don't meet every single one of the requirements, so don't be put off if you don't match them perfectly.

Interviews are planned to be on week commencing 29th April 2024