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curious to deliver work that matters, your journey starts here! The Civil and Environmental Engineering Department at Carnegie Mellon offers a unique interdisciplinary program that enables you to develop
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Computer Science (EECS)/Energy Systems Innovation Center (ESIC) to develop high-fidelity grid-forming inverter models and high-voltage direct current (HVDC) models, and investigate their impact on the power
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computational models, systems, and AI/ML tools using algorithms and analytics for materials and related physical sciences for a broad range of energy, transportation, and advanced manufacturing applications. In
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Inria, the French national research institute for the digital sciences | Montbonnot Saint Martin, Rhone Alpes | France | 2 months ago
: speech processing, computer vision, machine learning, solid programmming skills interest in connecting AI with human cognition Prior experience with LLM, SpeechLMs, RL algorithms, or robotic platforms is a
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and human-based leadership. As organizations increasingly blend algorithmic and human decision-making, fundamental questions arise about how leadership operates, adapts, and creates value in this new
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Deadline 23 Jul 2026 - 22:00 (UTC) Country Germany Type of Contract To be defined Job Status Other Offer Starting Date 2 Jul 2026 Is the job funded through the EU Research Framework Programme? Not funded by
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quantum chemical methods and machine learning; developing quantum algorithms for computational chemistry on quantum computers; and applying existing and new computational methods to study multiscale
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-grade algorithms. Demonstrated ability to work on complex data analysis problems and deliver robust computational solutions. Excellent communication skills and a strong interest in interdisciplinary
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Computer Science, or another subject of relevance for the project. Documented knowledge and proven research experience in the area of designing algorithms and methods for data privacy and machine learning is
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artificial intelligence, advanced battery manufacturing, battery prognostics, and battery recycling. The successful candidate is expected to develop rigorous modeling, computational, and/or experimental