Senior Research Engineer, Power Systems/Renewable Energy Management/AI/Machine Learning

Energy Research Institute @ NTU invites applications for the position of Senior Research Engineer.

Key Responsibilities:

• Core expertise includes Data Science, Artificial Intelligence, Machine Learning, and DER Analytical Tools
• Design and develop deep learning/AI-powered DER analytics & renewable energy/load forecasting tools to enhance distribution grid operations
• Interval forecasting of renewable power sources & load forecasting tools suited to small geographical areas (e.g., at the level of customers or aggregation areas) to capture the uncertainty, and adequately addressing the requirements of robust/stochastic optimization formulations
• Research into state-of-the-art solutions in AI-powered DER analytics, deep learning architectures, and generate innovative ideas that can be implemented for distribution grid management
• Be a hands-on technologist and domain/industry expert in commercial solution development, system integration & implementation
• Be an R&D team lead to define & achieve the project goals
• Work closely with researchers, developers, partners and/or external vendors for timely project delivery
• Any other ad-hoc duty or responsibility; e.g., project presentation, progress report preparation, procurement and research collaborations etc. assigned by the project supervisor

Job Requirements:

• Master’s or PhD degree in Electrical/Computer Engineering
• Specialized in Power Systems/Renewable Energy Management/Artificial Intelligence/Machine Learning
• Artificial Intelligence/Reinforcement Learning-based analytics & forecasting for distribution grid operations and energy management
• Actor-critic methods, Markov Decision Processes, Monte Carlo Tree Search, Policy Gradients, model-based acceleration, and deep learning (neural networks)
• Proven skills in Machine Learning (Supervised/Unsupervised/Semi-supervised/Reinforcement Learning); e.g., linear/logistics regression discriminant analysis, bagging, Support Vector Machine (SVM), Random Forest (RF), Bayesian model, neural networks, etc
• Programming languages (e.g., R, Python) in view of the abovementioned analytical concepts and techniques
• Strong skills in the use of current state of the art machine learning frameworks such as Scikit-Learn, H2O, Keras, Pandas, Numpy, TensorFlow and Spark, etc
• Experience with building and maintaining Big Data, using open-source technologies such as Hadoop and Cassandra
• Minimum 5 years of experience

We regret that only shortlisted candidates will be notified.