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in theoretical physics and mathematical physics, and the development of mathematical or physical theory of deep learning. The researcher will perform cutting-edge research in an intellectually
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communication and collaborative skills. Experience with SLAM, sensor fusion, LiDAR/depth camera data processing. Familiarity with deep learning for obstacle avoidance (e.g., map-less navigation). Background in
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learning, or deep learning models Salary Information Commensurate with experience Review Date March 10, 2025 Additional Information The successful candidate will be required to have a criminal conviction
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skills. Excellent programming skills in Python and Julia with experience with deep learning frameworks (e.g., PyTorch, TensorFlow). Experience building complex software systems, preferably with industry
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ability to analyze large datasets Knowledge of coastal and nearshore processes Preferred Qualifications: Proficiency in statistical modeling and time series analysis Experience with machine learning or deep
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tools that help to solve an environmental challenge Growing as a scholar (20%) Working closely with our team of data scientists and software engineers and finding ways to both learn from their approaches
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weekly working time of 40 hours per week. The position can be filled on a part-time basis. Background: Addressing climate change and biodiversity loss requires a deep understanding of global land-use
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, Coastal Marine Science, Computer Science, Electrical Engineering, or a closely related field. The ideal candidates will have experience in one or more of the following topics: deep learning for image and
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problem solving strategies used in nature and to ground these ideas by fostering deep collaborations with experimental biologists. Most recently, we have been interested in neural circuit computation and
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the developmental rules underlying phenotypic variation. The successful postdoctoral fellow will develop and implement an empirical framework that utilizes data-driven algorithms to learn relationships between past