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, and curated semantic datasets for analytics and downstream consumption. Develop scalable transformation and modeling layers using dbt (or equivalent) and data modeling best practices (Kimball
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manipulation systems for contact-rich tasks Key research challenges include: Learning reliable predictive models from sparse and noisy sensory data Incorporating semantic priors into planning and control
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Your Job: In an interdisciplinary team, you will implement approaches for the automated, large-scale availability and integration of energy system data and models, applying data science methods
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17 Apr 2026 Job Information Organisation/Company IMT Atlantique Department Doctoral division Research Field Computer science » Other Researcher Profile First Stage Researcher (R1) Positions PhD
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or more relevant platforms or technologies, such as: Copilot Studio o Power Automate / Power Apps, Power BI and semantic data models, Dataverse or relational databases, APIs, REST/JSON integrations Python
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enterprise data into usable models (Snowflake preferred). Experience working with large-scale administrative systems such as Banner, Workday, Salesforce, or ServiceNow. Effective analytical thinking and
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, governance structures, and integration patterns, ensuring that data is trustworthy, discoverable, interoperable, secure, and prepared for advanced use cases such as RAG, vector search, semantic layers, and
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sensing data as appropriate. ● Demonstrated experience using generative AI tools (e.g., large language models) effectively, including prompt design, evaluation of outputs, and responsible use in
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integration patterns, ensuring that data is trustworthy, discoverable, interoperable, secure, and prepared for advanced use cases such as RAG, vector search, semantic layers, and agentic workflows. *Applicants
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, and curated semantic datasets for analytics and downstream consumption. Develop scalable transformation and modeling layers using dbt (or equivalent) and data modeling best practices (Kimball