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Location: Toronto, Ontario, Canada Job ID: R0116509 Date Posted: Company Name: HITACHI ENERGY CANADA INC. Profession (Job Category): Administration & Facilities Job Schedule: Full time Remote: Yes Job Description: The Opportunity Hitachi Energy is seeking a highly motivated Research Intern to contribute to the design and evaluation of explainability and governance-aligned reporting frameworks for machine learning models used in power grid applications. The hired intern will work under the supervision of Research Scientist(s) in the Hitachi Energy Research center. This position will be held in a hybrid-format or remotely within Canada. Duration of internship : 4 months (can be extended up to 6 months as per need) Start date : April 6, 2026 (flexible) Mode of work : Remote or Hybrid (flexible) Renumerated Internship How you''ll make an impact Conduct literature reviews and comparative evaluations on ML explainability and trust-building techniques. Investigate frameworks for model transparency, reproducibility, and traceability. Explore and evaluate explainability methods (e.g., SHAP, LIME, surrogate models) and their applicability to industrial ML workflows. Prototype research concepts using Python and modern ML tooling. Document findings and present them in technical reports or publications, as appropriate. Your background PhD students/candidates in Computer Science/Engineering, Machine Learning, Software/Electrical Engineering. Excellent senior master''s (thesis-based) students with relevant experience are also welcome to apply. Solid understanding of machine learning & software engineering concepts, model evaluation, validation pipeline and CI/CD is required. Familiarity with interpretability techniques and/or model governance topics (e.g., explainability, reproducibility, fairness). Proficiency in Python and experience with ML libraries (e.g., Scikit-learn, TensorFlow/PyTorch, SHAP, LIME). Research experience involving defining problems, exploring potential solutions, and analyzing results, with the ability to clearly present findings to key stakeholders. Good communication skills (both written and spoken). Preferred qualification If you have any of the following qualifications, please ensure to clearly mention them in your resume. Prior experience working with interpretability or explainability libraries. Familiarity with AI governance frameworks (e.g., EU AI Act, AI Verify, NIST AI RMF). One or more first-authored publications in top AI/ML conference/journals. Ability to do critical and innovative thinking. Ability to take lead in realizing ideas. Experience with model-driven software engineering. Only selected applicants will be contacted. L''offre Hitachi Energy est la recherche d''un stagiaire en recherche hautement motiv pour contribuer la conception et l''valuation de cadres de rapport aligns sur l''explicabilit et la gouvernance pour les modles d''apprentissage automatique utiliss dans les applications de rseaux lectriques. Le stagiaire recrut travaillera sous la supervision d''un ou plusieurs chercheurs scientifiques au centre de recherche Hitachi Energy. Ce poste sera occup dans un format hybride ou distance au Canada. Dure du stage : 4 mois (peut tre prolong jusqu'' 6 mois selon les besoins) Date de dbut : 6 avril 2026 (date flexible) Mode de travail : distance ou hybride (flexible) Stage rmunr Votre impact Raliser des analyses documentaires et des valuations comparatives sur l''explicabilit du langage machine et les techniques visant instaurer la confiance. tudier les cadres permettant d''assurer la transparence, la reproductibilit et la traabilit des modles. Explorer et valuer les mthodes d''explication (p. ex., SHAP, LIME, modles de substitution) et leur applicabilit aux flux de travail industriels de l''apprentissage machine. Prototypes de concepts de recherche l''aide de Python et d''outils modernes d''apprentissage machine. Consigner les conclusions et les prsenter dans des rapports techniques ou des publications, selon le cas. Votre exprience tudiants/candidats au doctorat en informatique/ingnierie, apprentissage machine, gnie logiciel/lectrique. Les tudiants la matrise (avec thse) ayant une excellente exprience dans le domaine sont galement invits postuler. Une solide comprhension des concepts d''apprentissage automatique et d''ingnierie logicielle, de l''valuation des modles, du pipeline de validation et du CI/CD est requise. Connaissance des techniques d''interprtabilit et/ou des questions de gouvernance des modles (par exemple, explicabilit, reproductibilit, quit). Matrise de Python et exprience des bibliothques ML (par exemple, Scikit-learn, TensorFlow/PyTorch, SHAP, LIME). Exprience de la recherche impliquant la dfinition de problmes, l''exploration de solutions potentielles et l''analyse de rsultats, avec la capacit de prsenter clairement les conclusions aux principales parties prenantes. Bonnes comptences en communication ( l''crit et l''oral). Qualifications souhaites Si vous possdez l''une des qualifications suivantes, veuillez les indiquer clairement dans votre CV. Exprience pralable dans l''utilisation de bibliothques d''interprtabilit ou d''explicabilit. Connaissance des cadres de gouvernance de l''IA (par exemple, EU AI Act, AI Verify, NIST AI RMF). Une ou plusieurs publications en tant qu''auteur principal dans des confrences/revues de premier plan sur l''IA/le langage machine (ML.) Habilets mener une rflexion critique et innovante. Capacit prendre l''initiative pour concrtiser des ides. Exprience en ingnierie logicielle base sur des modles. Seuls les candidats slectionns seront contacts. Qualified individuals with a disability may request a reasonable accommodation if you are unable or limited in your ability to use or access the Hitachi Energy career site as a result of your disability. You may request reasonable accommodations by completing a general inquiry form on our website. Please include your contact information and specific details about your required accommodation to support you during the job application process. This is solely for job seekers with disabilities requiring accessibility assistance or an accommodation in the job application process. Messages left for other purposes will not receive a response.