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Your Job: We are looking for a PhD student in machine learning to work within a project linked to the “Helmholtz School for Data Science in Life, Earth and Energy (HDS-LEE)”. Your Job: Develop 3D+t
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para Ciência e Tecnologia; - PhD in Electrical Engineering, Computer Science, or equivalent scientific areas - Excellent academic and practical background in machine learning, deep learning, natural
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FPGAs, CGRAs, and many Machine Learning accelerators, offer significant opportunities for improving performance and energy efficiency compared to traditional CPUs/GPUs. Yet, porting and optimizing code
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National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | about 13 hours ago
to constrain the representation of aerosols in the NASA GEOS Earth System Model. Activities that would be involved in this project include (but are not limited to): Implement machine learning transfer learning
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and partners (that range from Microsoft Research to the NHS). For this project you should have a strong interest in AI/Machine Learning as well as an ability to develop, build and test interactive
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Group, https://www.chalmers.se/en/departments/e2/research/communications-antennas-and-optical-networks/communication-systems/ , employing around 45 people, including 8 faculty and 25 PhD students. During
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technical specifications. Knowledge, Skills, and Abilities: Advanced applied statistics skills, such as distributions, statistical testing, regression, etc. Professional experience developing machine learning
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(IST) Experience Early Skills and Qualifications PhD in Physics, Electrical and Computer Engineering Degree PhD Employment Type Full Time About the position A research position for one Research Grant
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Your Job: We are looking for a PhD student in machine learning to work within a project linked to the “Helmholtz School for Data Science in Life, Earth and Energy (HDS-LEE)”. Your Job: Develop
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. Into the second year, the project moves toward methodology refinement and Machine Learning integration. The student will execute a more ambitious cycle with a complex alloy system and integrate machine learning