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mathematical methods, algorithms, and applications are required. Simulators are a recognized method for architectural design explorations and the implementation of software development platforms. The goal
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construction, and a robust foundation in statistical spectral analysis, including familiarity with (or strong interest in) chemometrics and/or machine learning algorithms. Job requirements The Ideal Candidate
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increasing environmental awareness. However, most existing hearing aid algorithms optimize for only one of these objectives at a time, often at the expense of the others. To enhance hearing aid performance
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, algorithmic aspects, such as Parallelization, Fisher Information and Maximum Likelihood Estimation, with optical theory. Department of Imaging Physics Research in the department of Imaging Physics is aimed
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strong background in machine learning, computer vision, or data-driven modeling. You have extensive experience in the development and implementation of AI and machine learning algorithms, ideally with
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motivation to independently formulate research projects and carry them through to completion; Mathematical skills and command of standard software packages for implementing of optimization algorithms, and a
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advanced computer vision, dedicated lighting systems, optical communication, and robust control and guidance algorithms. You will work on: Design and implement computer vision algorithms for accurate landing
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on wind turbines and ships. This involves advanced computer vision, dedicated lighting systems, optical communication, and robust control and guidance algorithms. You will work on: Design and implement
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-guided medical applications, with a focus on advanced robotics. You will work directly with clinical data to design robust, efficient deep learning algorithms that maximize the information extracted from
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algorithms. We welcome applications from individuals with experience in: Experience developing deep learning models for real-time image/video segmentation, object tracking, reinforcement learning. Deep