H-E Parts
Research Intern in Time Series Data Analytics
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Job Description
- Req#: R0080015
Analyze, clean, process, and interpret power grid specific data.
Apply and modify ML methodologies to develop solutions for power grid automation.
Train, evaluate, update and modify machine learning models.
Prepare technical report and presentation on work accomplished during internship.
Master’s or PhD students in Electrical Engineering, Computer Science/Engineering, Applied Mathematics or related fields.
In-depth understanding of advanced AI/ML methodologies, particularly in the field of data mining and/or time series analysis and forecasting.
Excellent coding skill in Python and ML and data science related libraries.
Prior experience in data mining, discovery and wrangling and/or developing machine learning based solutions for academic or industrial problems is preferred.
Good communication skills (both written and spoken).
Nice to have- experience with distributed/parallel computing, familiarity with power grid automation, prior experience with power grid specific forecasting model developments.
Location:
Toronto, Ontario, CanadaJob ID:
R0080015Date Posted:
2025-03-03Company Name:
HITACHI ENERGY CANADA INC.Profession (Job Category):
Administration & FacilitiesJob Schedule:
Full timeRemote:
YesJob Description:
The Opportunity
Hitachi Energy has an opening for the position of Research Intern in the field of time series data analysis. The hired intern will work under the supervision of Research Scientist(s) in the Hitachi Energy Research center primarily at Montreal, Canada. This position will be held in a hybrid-format or remotely within Canada.
Duration of internship: 4 to 4.5 months
Start date: May 5th or earlier (flexible)
Mode of work: Remote or Hybrid (flexible)
Renumerated Internship
How you’ll make an impact
Your background
Only selected applicants will be contacted.
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About the company
H-E Parts International offers solutions in support of surface mining equipment fleets, crushing and materials processing and mobile construction equipment fleets.