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] Subject Area: Statistics and Machine Learning Appl Deadline: 2025/12/01 11:59PM (posted 2025/09/04, listed until 2026/02/01) Position Description: Apply Position Description The Department of Mathematics
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, creative start-ups, big data, big ambitions, hands-on learning, and a whole lot of robots, CMU doesn’t imagine the future, we invent it. If you’re passionate about joining a community that challenges the
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involve the integration of: Advanced motion planning and control algorithms Multi-modal perception techniques (e.g., vision, tactile, force) Machine learning models for physical behavior prediction and
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a team More specifically: - For mission 1: knowledge of signal and image processing, machine learning (PyTorch or TensorFlow + NumPy/SciPy), statistical processing & data and results visualisation
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Max Planck Institute for Astrophysics, Garching | Garching an der Alz, Bayern | Germany | about 1 month ago
based on a combination of novel simulation techniques, Bayesian statistical methods and machine learning approaches. The successful candidate will work closely with Prof. Dr. Volker Springel, the director
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28 Nov 2025 Job Information Organisation/Company Delft University of Technology (TU Delft) Research Field Educational sciences » Education Educational sciences » Learning studies Engineering
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, creative start-ups, big data, big ambitions, hands-on learning, and a whole lot of robots, CMU doesn’t imagine the future, we invent it. If you’re passionate about joining a community that challenges the
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develop machine learning approaches (deep learning) to understand the eco-evolutionary mechanisms underlying biological diversity from environmental (phylo)genomic data. - Methodological developments in
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at the top venues of machine learning research. Responsibilities and qualifications You should have prior experience with machine learning from both a theoretical and practical perspective. Experience in one
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project include two aspects: (1) based on the cutting-edge technologies from deep learning, computer vision or physics-informed machine learning, develop robust surrogate forward models to predict