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). Duties and Responsibilities Develop deep learning methods for complex geospatial and environmental problems. Develop geospatial foundation models using large-scale and heterogeneous geospatial data
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Inria, the French national research institute for the digital sciences | Paris 15, le de France | France | 1 day ago
of the ERC Starting Grant DYNASTY (Dynamics-Aware Theory of Deep Learning). The position might include traveling to conferences for paper presentation. Travel expenses will be covered within the limits
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support scientific discovery and real-world applications. We now invite applications for the captioned post(s). Duties and Responsibilities Develop deep learning methods for complex geospatial and
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Offer Description We are looking for a PhD student to join our Signal Theory and Statistical Learning group at TU Darmstadt, Germany. The position is in the mathematical foundations of deep learning, with
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control with Git). Professional experience 40%: Research experience in the tasks to be carried out. Verifiable experience in the development of reliable, explainable, unbiased and verifiable deep learning
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models using general-purpose foundational models. Use of deep learning libraries: PyTorch. Experience publishing scientific papers at prestigious conferences on machine learning and AI. Other criteria 20
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integration; working knowledge of ROS or ROS 2; practical experience training and evaluating deep learning models in PyTorch; demonstrated ability to debug across the full stack: hardware, drivers, middleware
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companies. All 15 Ph.D. projects fall within the overall theme of deep nanoscale physics, spanning material fabrication, measurements, and theory & simulations at the deep nanoscale. This Ph.D. project will
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Maritime Ltd and the University of Southampton. Find out more about Knowledge Transfer Partnerships here: https://www.ktp-uk.org/ Compute Maritime Ltd is a London-based deep-tech company bringing
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classification pipelines. Technical Skills: Proficiency in Python, deep learning frameworks (specifically PyTorch), and convolutional neural networks (CNNs) for image classification. Experience: Prior hands