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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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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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programming such as Python, R, MATLAB, or other similar programs and experience in using simulation/optimisation models and advanced data handling techniques e.g. machine-learning techniques, statistics
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and scope of MS research. This PhD project employs advanced network analysis and Large Language Models to develop predictive models for MS progression. It involves constructing and analyzing a complex
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want to hear from you! Your Job: Work on a wide range of computer vision and machine learning methods and applications focusing on the aspects outlined above, inspired by the needs of societally relevant
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The AITHYRA-CeMM Joint International PhD Call in Molecular Medicine and Artificial Intelligence (m/f
-20 fully funded PhD positions here: https://apply.cemm.at/ Supported by the Medical University of Vienna, the Technical University of Vienna and University of Vienna, the AITHYRA and CeMM PhD programs
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equipment in metabolomics, cell biology, time-lapse microscopy. You will work in a dynamic and highly interdisciplinary team including computer scientists, experimentalists and clinicians. You will be
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the functional work on NED. Experience with Machine Learning or Artificial Intelligence. Proficiency in Python, working experience of Astropy. Experience manipulating large, diverse and complex data sets
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the bachelor’s programmes at the Department of Chemical Engineering. In addition, you will conduct research and teach innovative approaches to solving problems in chemistry and process engineering using big data
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Your Job: In this position, you will be an active member of the SDL “Fluids & Solids Engineering” and will collaborate strongly with the SDL “Applied Machine Learning”. You will have the following