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dynamic workloads, while bridging theoretical research with practical implementation through close collaboration with cross-functional teams. Job Description: Conduct cutting-edge research in cloud
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, and maintainable. MLOps & Cloud Proficiency: Fluent in the essential MLOps toolkit, including Git, Docker, and CI/CD principles. Have experience building data and model pipelines on at least one major
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, and a constructive team-oriented mindset Excellent written and verbal communication skills in English Preferred Familiarity with cloud-based integrated development environments (IDEs) as well as APIs
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interfaces and dashboards within cloud computing environments. To develop and deploy AI-driven modules across cloud and on-premises environments, supporting end-to-end integration, testing, and delivery
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systems. Lead end-to-end delivery of cloud-native, high-availability applications from architecture to production, to guide engineering teams while aligning technology execution with business and research
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efficient AI techniques that enable scalable deployment on cloud, edge, and robotic platforms. The Research Assistant will contribute to the University’s mission by conducting high-impact research, developing
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Familiarity with ESG (Environmental, Social and Governance) regulations and standards Good written and oral communication skills Proficiency in Python and cloud computing Team player and able to develop
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applications is preferred. Knowledge in test instrumentation, PLC programming and cloud-based/IoT applications is preferred Job Requirements: Masters degree in engineering (e.g. Mechanical, Clean/Renewable
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that is clean, modular, and maintainable. MLOps & Cloud Proficiency: Fluent in the essential MLOps toolkit, including Git, Docker, and CI/CD principles. Have experience building data and model pipelines
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building energy models. The role will focus on participating in the development and implementation strategy of a cloud AI-based framework to perform business valuation. Key Responsibilities: Develop new CFD