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Field
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participate to scientific life of the teams. 3- Profile and skills required We are looking for a candidate with competences in Artificial Intelligence models, medical image processing, and mathematical modeling
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fields for several applications in the field of computer vision and inverse problem [SLX+21]. As far as the modeling of data term between distributions is concerned, one idea would be also to follow
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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
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Discrete geometric representations such as meshes are a crucial part of engineering simulation pipelines. The success and fidelity of numerical methods heavily depend on the accurate representation
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has a wide range of applications in domains focused on monitoring and securing complex systems, including mobility, manufacturing, communication, economics, and environmental science. These domains
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master’s degree in mathematics, physics or informatics with a strong knowledge in machine learning. Skills: Coding in Python and/or R is required. Previous knowledge in archaeology and zoo-archaeology would
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competences in Artificial Intelligence models, medical image processing, and mathematical modeling (Master 2 level). We seek solid programming and IT skills, along with good communication abilities and an
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. Required Skills and Candidate Profile The project is intended for a candidate with: ➢ Skills in medical image processing and deep learning adapted to clinical applications. ➢ A good knowledge of Python
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computer science or a related field. General: Knowledge of hospital environments and the healthcare sector, as well as innovative technologies: AI algorithms, image and signal processing, segmentation, modeling
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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