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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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computing. With extensive experience in medical image analysis, computer vision, and AI systems through collaborations with leading institutions. Key Responsibilities: Conduct advanced research in the areas
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workflows. (Preferred) Basic skills in microscopy image analysis and quantitative data processing. To Apply Applicants should submit: A CV including publications A cover letter describing relevant experience
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in image processing, quantitative analysis, and biological interpretation Proficiency in AI/machine learning tools for image segmentation, transformation, registration, or tracking Solid mathematical
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accelerating its practical adoption in construction and beyond. The Research Fellow is expected to: • Assist the design and lead the commissioning and operation of the new metal additive manufacturing
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related field with emphasis on biosignal analysis and imaging systems. Proven experience in visual behavior analysis, video processing, or physiological signal interpretation, preferably in aquatic
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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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with ultrastructural analysis. Manage, process, and analyze large imaging datasets generated from the project. Assist in mentoring and training PhD and undergraduate students in relevant techniques and
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. Write the report for the project progress. Work with research assistant for the prototype. Job Requirements: PhD in Electrical and Electronic Engineering, Computer Engineering / Science, or related field