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-computing hardware Work in an interdisciplinary team of engineers, computer scientists, and life scientists Regularly participate in international conferences to present your own work, and learn about state
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Python Arduino and C++ (Physical Computing) Creation of interactive objects and components Machine Learning and Natural Language Processing Specific Requirements Candidates must hold a PhD in engineering
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–classical algorithms or optimization methods Background in uncertainty quantification, reduced-order modeling, or machine learning Experience collaborating in interdisciplinary research teams A doctoral
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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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need for graduate student services to provide technical and/or analytical support in the field of computer science and electrical engineering for a computer vision team, specifically for the Shipboard
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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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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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experience with the integration of computer technology into instruction. Strong communication, organization, interpersonal, and counseling skills Willingness to travel and teach among four campus sites and
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self-adaptation capabilities. Three major challenges have been identified: (P1) modelling uncertain environments where robust, weakly supervised machine learning algorithms can be deployed to irrigate
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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