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Field
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). The emergence of data-driven techniques (broadly grouped under the term “machine learning”) challenges the traditional foundations of controls and represents an alternative paradigm that cannot be ignored
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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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biomedical image processing. Within the scope of machine learning and computer vision, there will be freedom to suggest your own research directions, and to become acquainted with new techniques and approaches
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(analysis/ geometry), applieddifferential geometry and machine learning;•Solid publication record in image analysis, scientific computing or machine learning.•Knowledge and practical experience in at least 3
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approaches. What we ask of you Required PhD in machine learning, physics, or a related field. Established expertise in deep learning (familiarity with graph neural networks, transformers, diffusion and flow
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to your expertise. What do we require? A PhD degree (or equivalent qualification) in AI (e.g., machine learning, natural language processing or computer vision); A strong scientific track record, documented
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of the Swammerdam Institute for Life Sciences at the University of Amsterdam (UvA). BDA works on the development of methodology for data mining, machine learning/deep learning, data fusion, and modelling and
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work to discover cell specific isoforms and relate them to pathological signatures and genetic risk factors in ALS patient tissue. You will apply supervised and unsupervised machine learning methods
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needed. You have experience in advanced bioinformatic analyses of omics datasets, preferably single cell and/or long read RNAseq data. Demonstrable experience in advanced machine learning and/or high
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of you Required PhD in machine learning, physics, or a related field. Established expertise in deep learning (familiarity with graph neural networks, transformers, diffusion and flow based generative