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qualifications, experience and competence: A higher degree in Artificial Intelligence, Automation Engineering, Computer Science, Computer Security, Cosmetic Science and Technology, Electronic Engineering
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-order modeling, or machine learning Experience collaborating in interdisciplinary research teams What you will do Develop hybrid quantum–classical methods to improve simulation and prediction
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: Investigate and design optimal computing and communication architectures for hardware acceleration of large-scale machine learning workloads Perform characterization and modeling of electronic and optical
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acceleration of large-scale machine learning workloads Perform characterization and modeling of electronic and optical devices Develop hardware-aware machine learning models incorporating electronic and optical
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Aerospace can be found at: https://www.issaerospace.com/ . Qualification / skills we require: A good degree in Aerospace, Mechanical, Electrical Engineering or a closely related field. Strong technical
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inverters to enhance grid flexibility, reliability and stability. • Apply machine learning and AI tools for the battery system health estimation and maintenance prediction and integrate analytics
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position in the area of Learning, Optimization, and Decision Analytics. SCAI (https://scai.engineering.asu.edu/ ), one of the eight Fulton Schools, houses a vibrant Industrial Engineering and Computer
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an experienced BAS-focused engineering professional to provide engineering design, standards, and program oversight for campus Building Automation Systems that support learning, research, and campus life. In
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experiences. A Ph.D. in Electrical Engineering, Computer Engineering or a closely related field, is required by the appointment start date. Penn State Behrend (http://www.behrend.psu.edu ) is a comprehensive
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Inria, the French national research institute for the digital sciences | Montbonnot Saint Martin, Rhone Alpes | France | 10 days ago
-PULSE addresses a key open question in responsible AI: can we design practical machine learning systems that satisfy strong privacy guarantees [1] and fairness [2] constraints simultaneously, without