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techniques for data storage/retrieval/processing/visualization on large scale. Cybersecurity Software engineering Machine/deep learning Technical aspects of human computer interaction (HRI, multi-modal
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computational modeling and/or analysis of complex biological systems, integrating state of the art tools such as machine and deep learning approaches. Experience in managing biological databases and statistical
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of and experience with hydrological and/or hydrogeological modeling - Knowledge of and experience with AI concepts (machine learning, deep learning, and PINNS) and/or digital twin development - Experience
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, clustering, classification • deep learning, variational auto-encoding, back-propagation • excellent knowledge of R and Python. The knowledge of C++ would be a plus. Application: Application files should
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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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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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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