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Field
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deep learning, preferably including some exposure to graph neural networks or geometric deep learning. Proven experience with implementing machine learning methods in Python and Pytorch. Familiarity with
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experimental data (from ex-situ and in-situ measurement). Therefore, she/he will develop a way to optimize/guide the experiments trough artificial intelligence approach (machine/deep learning) that he will
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). The candidate should have hands-on experience developing state-of-the-art machine learning models, particularly deep neural networks (experience with graph neural networks is highly valued). Their background
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complete) an M.Sc. (or equivalent) in Computer Science or a related discipline ML expertise: You have strong programming and deep learning experience (e.g., PyTorch, TensorFlow), backed by a substantial
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apply a fast and efficient forest trait mapping and monitoring method based on the Invertible Forest Reflectance Model. A machine learning / deep learning framework will be explored and developed
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, for example, Machine Learning, Deep Learning, Artificial Intelligence, Information Security, or related subjects. Alternatively, you have gained essentially corresponding knowledge in another way. The position
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, visiting researcher opportunities, access to modern GPU clusters for deep learning research, and strong academic-industry connections. CADIA's commitment to open science aligns perfectly with this project's
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substrates while advancing our understanding of deep learning through dynamical systems theory. You will work with two cutting-edge experimental systems: (1) light-controlled active particle ensembles
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Can We Teach AI to Outsmart Humans in the Werewolf Game—Without Changing the AI Itself? Large Language Models (LLMs) have dazzled us with their ability to converse, code, and create—but they still
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reducing demand. However, demand drivers are manifold, including technology advancements, population and economic trends, and their future developments come with deep uncertainties. Infrastructure policies