2,823 algorithm-development-"Multiple"-"Simons-Foundation"-"Prof"-"UNIS" Fellowship positions
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: image processing, machine learning, and patient records. Track record of development and implementation of novel machine learning algorithms in the healthcare setting or other spaces. Extensive experience
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This research project aims to establish the theoretical and algorithmic foundations of quantum adversarial machine learning, an emerging field at the intersection of quantum computing and machine learning. It
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Developing and integrating AI algorithms into the real development progress Preparing academic publications such as patent applications and research papers Contributing to quarterly and annual report writing
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scientists, biomedical informaticians, clinicians, and public health researchers to develop deployable, trustworthy methods that improve patient outcomes and health system operations. Key responsibilities
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-edge research in machine learning and automated reasoning for safe algorithmic systems. The Research Fellow will be responsible for developing advanced theory and machine learning algorithms
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will work closely with the Principal Investigator (PI), Co-PI, and the research team to develop deep learning-based computer vision algorithms and software for object detection, classification, and
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to develop a decentralised waste-to-energy plasma unit capable of producing synthesis gas for electricity and heat. The innovation builds upon original research and integrates multiple stages (plasma
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Institute of Systems and Robotics-Faculty of Sciences and Technology of the University of Coimbra | Portugal | 16 days ago
-FEDER-00818200 and the acronym flexREC, financed by FCT, the European Regional Development Fund and national funds through the program MPr-2023-12l, in the following conditions: Scientific area
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, including machine learning, computer vision, adaptive data modelling, and computational imaging. The objective is to develop state-of-the-art machine learning algorithms for solving ill-posed inverse problems
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, including machine learning, computer vision, adaptive data modelling, and computational imaging. The objective is to develop state-of-the-art machine learning algorithms for solving ill-posed inverse problems