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(optimal) solutions—with subsymbolic approaches such as deep learning and reinforcement learning to reduce the complexity of knowledge acquisition and search for solutions. Therefore, this project is closely
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informatics, or a related field - Strong programming skills in Python and experience with deep learning frameworks (PyTorch preferred) - Experience or strong interest in large language models, multimodal
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, engineering, physics, biophysics, applied mathematics, computational biology or a related quantitative field Strong background in deep learning for image analysis / computer vision, ideally on microscopy time
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Autónoma de Madrid, and funded by the Community of Madrid. Among the tasks to perform are: Management and preprocessing of audio databases. Design, implementation, and testing of deep learning algorithms
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application! We are looking for a PhD student in biomedical engineering with a focus on deep learning for medical images Your work assignments The position focuses on developing methods for federated learning
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The University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 9 days ago
position will include, but is not limited to, multimodal+embodied semantics, human-like language generation and Q&A/dialogue, and interpretable and generalizable deep learning. The duties of the postdoctoral
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such as Machine Learning, Natural Language Processing, AI in Education, Knowledge Representation, and Python-based analytical seminars at the BSc, MSc, and PhD levels. Responsibilities include assisting in
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novel machine learning models—including Physics-Informed Neural Networks (PINNs), variational autoencoders, and geometric deep learning—to fuse multimodal data from diverse experimental probes like Bragg
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applications for faculty positions in Computer Science. Faculty specialising in data science, machine learning (deep learning, reinforcement learning, multimodal learning), Generative AI, and computer graphics
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the field of frugal or green AI TECHNICAL SPHERE You have a proven experience in frugal, green or low-resource AI Strong grasp of deep learning architectures (CNN, RNN, Transformers, LLMs). Experience in fine