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are particularly interested in candidates who combine computational biology, data science, and machine learning/AI with deep biological insight. While wet lab activities are welcome, they are not mandatory. However
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, are discriminant). In particular, point i) undermines most of the recent deep learning machinery used for shapes classification [e.g. PointNet Qi et al., 2017], even if one wished to adopt them for simple feature
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on their vulnerabilities against those attacks. While, the existing recent literature on the study of such attacks for FL mostly concentrates on deep learning. The PhD candidate will also investigate different ML algorithms
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of the moving sources, and directionality of the DAS measurements, make the use of machine learning techniques very appealing. The doctoral student will propose deep learning methods for source separation of DAS
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patient clusters and digital phenotypes, leveraging machine learning approaches to identify individuals at high CV risk based on clinical and biochemical markers, immune markers, digital health data (e.g
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computation of visibility for the whole domain is intractable due to its high computational complexity, we will explore leveraging machine learning techniques such as reinforcement learning for the efficient
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Master/engineer degree in computer science, applied mathematics, data science with background in image processing, imaging inverse problems, deep learning and optimisation. Good coding skills for numerical
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. Communication efficient learning of deep networks from decentralized data. In Artificial Intelligence and Statistics, PMLR, 2017. [4] Rieke, N., Hancox, J., Li, W. et al. The future of digital health with
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.; Perdikaris, P.; Karniadakis, G. E. Physics-Informed Neural Networks: A Deep Learning Framework for Solving Forward and Inverse Problems Involving Nonlinear Partial Differential Equations. J. Comput. Phys. 2019
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The candidate should preferably have a PhD in Computer Science or Robotics with a solid background on deep learning and 3D scene understanding. Experience with LiDAR and Computer Vision is a plus. The candidate