Research Intern – Explainable Ai And Reporting Framework For Power Grid Machine Learning Applications
Hitachi Energy
Saint-Laurent, Canada
Hybrid
Ml explainability techniques
Model transparency, reproducibility, traceability
Python and ml libraries
The Research Intern will contribute to the design and evaluation of explainability and governance-aligned reporting frameworks for machine learning models used in power grid applications
Job Summary
The Research Intern will contribute to the design and evaluation of explainability and governance-aligned reporting frameworks for machine learning models used in power grid applications.
Responsibilities include conducting literature reviews, investigating frameworks for model transparency, exploring explainability methods, prototyping research concepts using Python, and documenting findings.
This is a remunerated internship for PhD students/candidates or senior master's students with relevant experience, offering a hybrid or remote work mode within Canada.
Matching Summary
The Research Intern will contribute to the design and evaluation of explainability and governance-aligned reporting frameworks for machine learning models used in power grid applications.
Skills & Requirements
Must-have
ML explainability techniques
model transparency, reproducibility, traceability
Python and ML libraries
research experience
communication skills
Nice-to-have
AI governance frameworks
critical and innovative thinking
taking lead in realizing ideas
model-driven software engineering
Key Requirements
PhD students/candidates or senior master's students