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learning-based computer vision algorithms and software for object detection, classification, and segmentation. Key Responsibilities Participate in and manage the research project together with the PI, Co-PI
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Requirements: PhD in geophysics, geology, geomatics, geodesy, geomatics, electrical engineering, computer science, natural hazards, or a related field Demonstrated skills in remote sensing, image processing
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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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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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, emulations, physical testbeds, and commercial networks. Collect and analyze KPIs such as throughput, latency, and reliability, and fine-tune network parameters to meet diverse QoS requirements. Prototype and
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create a collaborative and engaged user community. A special focus lies on the operation of the multiphoton and FLIM microscopy systems and establish intravital imaging pipeline. Main Duties and
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an exception basis, a two-year programme may be supported. Service Commitment One year for every year of sponsorship. Application Process Applications are open throughout the year via the various links below
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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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(EM) to join our multidisciplinary team. The successful candidate will lead EM-based investigations and contribute to the development of advanced imaging protocols. This position offers access