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Field
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for perceptual and creative relevance; Curate and/or utilize benchmark datasets of pareidolic visuals, and apply statistical and machine learning methods to analyze visual data and model behavior; Publish and
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statistical methods; will seek out and learn new methods to better solve problems > Experience with modern AI techniques and methods or desire to work on Applied Machine Learning Problems > Constantly questions
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architectures in the field of computer vision and with training, validating and inference processes in machine learning; Familiarity with generative AI; Curious about mathematics and biology; Excellent
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-GUIDE project, we will make directed evolution guidable and, ultimately, predictable by machine learning. Specifically, you will build a first-in-class framework to expedite the design of high-affinity
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mechanics at the atomic scale. In this project, the University of Groningen will develop an array of state-of-the-art machine learning potentials for multi-component alloy systems that are relevant
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of state-of-the-art machine learning potentials for multi-component alloy systems that are relevant for the new green steels compositions, including impurities and tramp elements. These models should enable
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advanced motion planning algorithms with machine learning techniques, such as reinforcement learning, imitation learning, and task generalization. You will focus on designing intelligent robotic systems
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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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Criteria A MSc degree in Computer Science, Statistics, Data Science, Artificial Intelligence, or a related field; Strong knowledge of and experienced with statistics, machine learning, and stochastic
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related to staff position within a Research Infrastructure? No Offer Description Are you looking for a PhD position where you develop state-of-the-art machine learning methods for the life sciences