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of structures, facilitating a form-finding process driven by FEM analysis. Training deep learning algorithms to suggest multiple structural concepts tailored to specific boundary conditions. Expanding FEM
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Social Data Science, Computer Science or related areas. Besides showing experience with machine learning, including deep learning, the candidate should be able to demonstrate knowledge about human
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The University of Alabama, Department of Electrical and Computer Engineering | United States | 2 months ago
applications. Active research by the UA ECE faculty includes robotics, intelligent sensors, computer vision, machine learning and deep learning, wearable sensors, security and privacy in computing systems
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machine learning, deep learning, and network optimization to develop a scalable and secure AI framework for smart transportation. The successful candidate will work with experienced researchers, gain access
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methods based on state-space models [3] have demonstrated strong capabilities in modeling very long sequences. In this context, these methods provide the perfect alternative to standard deep learning
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research project focused on leveraging deep learning and advanced image processing techniques to improve the current tools for biomonitoring of aquatic ecosystems. This position involves the development and
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, formatting and visualizing/interacting with big acoustic and satellite datasets. Collaborate with AI specialists to train and validate deep-learning models for biodiversity classification. Participate in field
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policymaking. CBS LAW is a unit under the Department of Business Humanities and Law (BHL) where there are two other units: Entrepreneurship, Ethics and Leadership; and Governance, Culture and Learning. The three
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Your job Are you looking for a PhD position where you develop state-of-the-art machine learning methods for the life sciences (geometric deep learning, transformer-based approaches, ...) with a
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we will apply state-of-the-art machine learning and deep learning techniques on open- access and collected datasets to determine how accurately these systems can identify dock plants under Norwegian