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Field
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Join us to explore the mechanics of soft matter through a unique blend of theory, hands-on experiments, and machine learning. Job description Soft matter such as polymers and hydrogels
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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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We’re seeking for motivated candidates that are interested in developing computer models of the composite human neuro-muscular system that combine detailed musculoskeletal geometries, muscle-tendon
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by the Fondation Paralysie Cérébrale—you will help develop a machine learning-based multimodal prediction tool for CP diagnosis and long-term outcomes. This tool will integrate advanced clinical
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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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individualized diagnosis and prognosis. As part of the ENSEMBLE Project—a multinational study funded by the Fondation Paralysie Cérébrale—you will help develop a machine learning-based multimodal prediction tool
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wind speed and sea ice extent. The baseline algorithms designed to estimate such variables from the observed data at Level 1 (L1) are based on Machine Learning (with the exception of that for Freeze/Thaw
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to: Development of computer models of the musculoskeletal system incorporating neural control pathways Train NMS model control policies via RL Your seconday tasks will include: Using learned NMS model control
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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