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, Computational Science, or a related field; a PhD or equivalent experience is preferred Strong programming skills in Python, with experience in machine learning frameworks such as PyTorch or TensorFlow
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the contribution of genetic and non-genetic driving forces for the cells’ evolution and glioma development. Using multi-omics data integration and machine learning, we will investigate cellular
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researcher with a proven track record in areas relevant to auto-tuning, focusing on ML-driven compiler optimization, transfer learning, and programming for heterogeneous systems across CPUs, GPUs, and
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the MONALISA network and local courses, (iii) to write scientific articles and a PhD thesis, as well as (iv) to teach and disseminate research in the scientific community. Requirements: Applicants should hold a
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training activities within the MONALISA network and local courses, (iii) to write scientific articles and a PhD thesis, as well as (iv) to teach and disseminate research in the scientific community
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interdisciplinary research, integrating molecular simulations, machine learning, statistical physics, multiscale modeling, and uncertainty quantification. By integrating state-of-the-art machine learning models
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related field Sound knowledge in the field of artificial intelligence and machine learning Ideally experience with knowledge graphs, semantic search, graph neural networks (GNNs), explainability
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offers the possibility of a doctorate. What you bring to the table Bachelors, Masters, or a PhD degree in Electrical Engineering or equivalent technical field > 1 years of related experience or equivalent
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with emphasis on medium access layer protocols (MAC), machine learning and LTE/5G procedures Excellent analytical understanding and research skills Creative thinking and interest in new technologies Self
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years of experience working with IEEE WiFi standards Strong background in wireless communication with emphasis on medium PHY or MAC, machine learning and WiFi procedures Excellent analytical understanding