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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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/02/03 11:59PM ** (posted 2025/12/11, listed until 2026/02/03) Description: Apply Description Call for Applications (Cornell PhD candidates and recent Cornell PhDs) Eligible applicants for this search
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Prof. M. Fernando Gonzalez-Zalba, a multidisciplinary and dynamic research team passionate about building a scalable quantum computer based on silicon technology. Moreover, the PhD will have a strong
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responsible machine learning and/or human-AI interaction can be integrated with music-based forms of human creativity to contribute to health and well-being. The doctoral research should address one or several
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of marine geophysical survey and machine learning algorithms; Job Requirements: A Bachelor degree in geophysics or equivalent and A PhD degree in geophysics / geomechanics or equivalent from a recognized
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to investigate how responsible machine learning and/or human-AI interaction can be integrated with music-based forms of human creativity to contribute to health and well-being. The doctoral research should address
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series analyses, (2) Earth or planetary remote sensing, (3) Data science approaches, including statistical methods, handling of large datasets, pipeline development and/or machine learning (4) Full stack
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position within a Research Infrastructure? No Offer Description Aalto University is inviting applications for a Postdoctoral researcher in molecular machine learning. The successful applicant will join the
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findings, and help attract funding. PhD in Computer Science, Computational Bioengineering, Mechanical or Electrical Engineering Excellent coding skills Preferably a familiarity with machine learning and deep
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. The candidate should have a PhD in Computer Science or a closely related field. Relevant background and skills include: Strong foundation in one of the following areas: Machine Learning / Information Retrieval