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, interoperability, and compliance with emerging grid standards. Key Responsibilities: Design and develop control algorithms for grid-forming converters. Conduct simulation and experimental validation using real-time
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Tableau to optimize reporting on data. Develop custom data models and algorithms to apply to data sets. Use predictive modeling to drive operational decisions and staffing to optimize business outcomes
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connect to our group’s work and how this position supports their career development goals. Possible research topics include (but are not limited to): Optimization algorithms for machine learning (stochastic
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algorithms. Excellent computer skills, including thorough familiarity with multiple programming languages, some experience in writing non-trivial programs, and familiarity with common algorithms, mathematical
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Chirayath, ACES specializes in remote sensing and Earth science technology innovation. The Software Engineer will assist in designing and refining algorithms like Active Fluid Lensing and MiDAR. This role
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research team. Key research areas include: Development of low-carbon materials and tunable thermal energy storage materials integrated with smart sensors and advanced algorithms Creation of Digital Twins
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candidate will contribute to: Developing supervised deep learning algorithms for 3D point clouds Developing self-supervised deep learning algorithms for 3Dpoint clouds Expand for a wider variety of downstream
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Job Posting Title: R&D Software Engineer ---- Hiring Department: Applied Research Laboratories ---- Position Open To: All Applicants ---- Weekly Scheduled Hours: 40 ---- FLSA Status: Exempt
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technologies for patient benefit. The post holder will play an active role across components of the theme. We believe that integrating artificial intelligence (AI) algorithms and state-of-the-art monitoring will
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Inria, the French national research institute for the digital sciences | Saint Martin, Midi Pyrenees | France | 8 days ago
follows a phased algorithm: 1) generate an initial training set by uniformly sampling input points 2) (re)train the model on the trainng set 3) use feedback from the model’s performance to generate/augment