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. For the development and implementation of new algorithms in the field of 3D model generation, scene design and visual effects and in the development of AR/VR applications, we are looking for committed, independently
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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
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annotation and preprocessing. Research and develop algorithms and AI models for various computer vision tasks of PCB image analysis. Algorithm/model evaluation and testing. Maintain the integrity of the data
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: Mathematical analysis skills to develop algorithms to implement system design requirements. Demonstrated ability to participate in research activities including manuscript production. Email to a Friend https
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, and use smart phone apps to collect passive and active data using a prospective observational cohort study design. We will use this data to develop and validate a personalised risk prediction algorithm
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and Electronics group in the Electrification Section of EEID/ ESTD, at Oak Ridge National Laboratory (ORNL). Major Duties/Responsibilities: Develop and deploy algorithmic tools for monitoring
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, and space hardware. This PhD research aims to develop a comprehensive Mode Selection Framework for Reduced Order Modelling (ROM) in Structural Dynamics—using machine learning to build robust
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focus will be on developing compact, efficient, and real-time LLM algorithms/hardware on the edge and developing demos for specific applications such as speech disorder therapy. Your responsibilities will
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team to conduct the research on development of AI models and algorithms for image processing, computational imaging as well as computer vision applications. The roles of this position include: Research