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; onboard computational hardware using the Robot Operating System), and experimental testing (on unmanned vehicle swarms). A solid competency with the standard implementation tools used in machine learning
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; onboard computational hardware using the Robot Operating System), and experimental testing (on unmanned vehicle swarms). A solid competency with the standard implementation tools used in machine learning
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Postdoctoral Research Associate - Radio Frequency Fingerprinting using Machine Learning Employer UNIVERSITY OF SYDNEY Location Sydney, Australia Salary $93,424 - $125,896 + 17% superannuation Closing
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Computer Science, Artificial Intelligence, Machine Learning, Deep Learning, Natural Language Processing, Computer Vision, Computer Engineering, Computational Science, Data Science, Management Information
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systems through data-driven modeling and optimization. The successful candidate will work at the intersection of thermal-fluid sciences, control theory, and artificial intelligence/machine learning to
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. Familiarity with modeling, simulation, and experimental validation of energy systems is expected. Preferred Qualifications: Experience with artificial intelligence and machine learning for energy
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Max Planck Institute for Physics, Garching | Garching an der Alz, Bayern | Germany | about 1 month ago
, experience with modern machine-learning techniques such as neural simulation-based inference or transformer architectures, and familiarity with fitting methods and tools. Participation in the hardware
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interdisciplinary project; we are open to work with researchers with varying academic backgrounds. Those with experience in, for example, machine learning, high-performance computing, reconfigurable hardware, cloud
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with researchers with varying academic backgrounds. Those with experience in, for example, machine learning, high-performance computing, reconfigurable hardware, cloud computing, compiler engineering
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chemical or biological sensing; Familiarity with signal processing, embedded systems, or machine learning; Experience with hardware–software co-design and system integration; Prior experience mentoring