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PhD in Computer Science, Engineering or other Machine Learning-related field. • Programming experience in python, C++ or other relevant language and experience in deep neural networks • Strong
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and programming skills and experience of computer vision and machine learning. Previous experience of real time systems development in Python, OpenCV, PyTorch and deep learning are essential. Experience
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Laboratoire de Physique des Interfaces et des Couches Minces (LPICM), UMR CNRS/École Polytechnique, | Palaiseau, le de France | France | 19 days ago
(denoising, Mueller matrix calculation/decomposition) and AI-based diagnostic algorithms using machine/deep learning. The primary challenge will be to deliver practitioner-relevant cervical images in under 0.5
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of Birmingham is inviting applications for a Research Fellow position focused on Machine Learning for Automated Formal Verification. Machine learning has transformed programming, with code generation rapidly
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Research Fellow in AI for Healthcare (1 FTE) – ArcHub ICUSafeNotes-School of Computer Science 011287
breakthroughs into commercial healthcare solutions. In the ICUSafeNotes project, we are conducting research and development of multimodal machine learning models that includes ICU note summarisation and analysis
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postdoctoral researcher with: A PhD (or near completion) in Computer Science, Computational Biology, Mathematics, Bioinformatics, or a related discipline. Proven expertise in machine learning and algorithm
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third largest university by intake in Singapore. SIT’s mission is to innovate with industry, through an integrated applied learning and research approach, so as to contribute to the economy and society
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Operations & Technology Knowledge Center, with the following conditions: MAIN FIELD:………..…………………………………………………………………………………………… Management, with particular focus on the intersection of: Machine Learning Causal
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. Researching and developing novel machine learning architectures for integration across multiple types of high-dimensional data. Researching and implementing novel algorithms for analysis of latent factors and
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for machine learning and artificial intelligence, with a strong emphasis on developing and applying models such as LSTM and other time-series analyses to predict the longevity and behaviour of bioactive