Research Intern - Electronic Systems Online Machine Learning

Description

Research Internship Job Posting

Hitachi Energy has an opening for the position of Research Intern in the field of online learning and its applications in electrical dynamical systems. The hired intern will work under the supervision of Research Scientist(s) in the North America (Canada) and Switzerland Hitachi Energy Research centers. As of now, this position will be held remotely in Canada.

Duration of internship

• Preferably 6 months, start and end dates are negotiable.

Responsibilities

• Perform literature survey to explore state-of-the-art online learning methodologies and their applicability, strengths, and weaknesses. The focus will be on methodologies and tools used in domains including (but not limited to) adapting to environmental (spatial and temporal) changes, re-trainability, etc.

• Investigate methodologies to understand and interpret the decisions from ML models for dynamical systems (focus will be on electrical dynamical systems).
• Apply and modify existing online learning methodologies to develop adaptive predictive models for dynamical systems.
• Do necessary data-preprocessing.
• Run experiments in python, prepare codebase.
• Prepare technical report, presentation on work accomplished during internship and/or publication in peer-reviewed conference.

Qualifications

• PhD students/candidates in Computer Science/Engineering, Electrical Engineering or Applied Mathematics. Excellent senior year Master’s students with relevant experience are also welcome to apply.
• Understanding of AI/ML methodologies, such as classical and deep learning based supervised/unsupervised classification/regression and associated models required
• Coding experience in Python and ML libraries (e.g., Tensorflow, Pytorch, Keras, Numpy, Scipy, SkLearn) required

• Prior experience in one or more of the following: applied machine learning, technology screening through literature survey, or writing technical articles/reports is preferred
• Familiarity with one or more of the following: continual learning, lifelong learning, meta learning, Physics Informed Machine Learning would be an asset
• Graduate level knowledge on probability, statistical methods, and optimization would be an asset.
• Ability to do critical and innovative thinking.
• Excellent communication skills (both written and spoken).