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

4 days left

25 Sep 2020
End of advertisement period
25 Oct 2020
Contract Type
Full Time

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.


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