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Field
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data to design robust, efficient deep learning algorithms that maximize the information extracted from images and delivered to the robot. To be successful in this role, we are looking for candidates
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, the next step in this project is to address sparse optimization for tensors. We propose the integration of randomized algorithms into sparse optimization frameworks for the purpose of completing
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service quality. To address these challenges, advanced methodologies and algorithms are needed to design effective revenue and inventory management strategies for complex stochastic systems. The growing
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data analysis and modelling on this project. Specifically, advanced imaging processing skills, which will allow them to develop and validate algorithms for combination of multiparametric / multi-scale
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conferences and journals. Overview: The successful candidate will join an interdisciplinary team focused on developing innovative numerical algorithms and software to address emerging challenges in scientific
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analysis, as many observed phenomena cannot be adequately modeled by stationary processes. The NOMOS project aims to develop a new generation of nonstationary models and algorithms for analyzing various
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gene gain/loss events, horizontal gene transfer, and functional diversification within gene families. You will apply statistical models and machine learning algorithms to identify associations between
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-disciplinary involving algorithmics, stochastic optimization, multi-criteria decision making, and data science. As part of the project, you will implement and test algorithms and further develop your skills in
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. Developing workflows and machine learning algorithms to accelerate catalyst design (optional). Group: Atomistic & Molecular Modelling for Catalysis Group Requirements Specific Requirements PhD in Chemistry
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evaluation of algorithms for: perception in robotics; sensor based control and navigation ; interactive mobile manipulation; multi-sensor data modelling and fusion. This job offer takes place within