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Field
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, Mathematics, Physics, or a closely related field. Proficiency in machine learning libraries (e.g, scikit-learn, PyTorch, and transformers) and data analysis tools (e.g., pandas, NumPy, and CuPy). Hands
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integration. Lead and contribute to research involving AI-powered and AI-enabled robotic systems, including deep reinforcement learning, computer vision, and human-robot interaction. Facilitate strategic
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deep learning frameworks (e.g., PyTorch, TensorFlow, and JAX). • Experience in PDE/ODE modeling and numerical methods. • Strong interest in interpretable ML and mechanistic model discovery. Submit a
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to improve our understanding of the formation and evolution of oceanic crust. Samples from several drilled and dredged areas are available in the CRPG collection (EPR: Hess Deep, MAR: Atlantis Massif, SWIR
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, Mathematics, Physics, or a closely related field. Proficiency in machine learning libraries (e.g, scikit-learn, PyTorch, and transformers) and data analysis tools (e.g., pandas, NumPy, and CuPy). Hands
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the possibility of yearly renewal subject to funding availability. Responsibilities • Design and implement deep learning architectures, AI agent pipelines, and computer vision algorithms to achieve project goals
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Max Planck Institute for Gravitational Physics, Potsdam-Golm | Potsdam, Brandenburg | Germany | 8 days ago
learning, astrophysics of compact objects and binary’s formation scenarios, cosmography with gravitational waves (including dark energy, dark matter, gravitational lensing), and tests of gravity in
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Learning and Deep Learning libraries (e.g., Pytorch, Tensorflow) will be considered a significant advantage; Previous experience in image processing and\or computer vision will be considered an advantage
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responsibilities: 1) The postdoc will work with the PI to develop a project in one of the two research directions, to comprehensively read literature, to establish appropriate experimental systems, to learn
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experience in deep learning frameworks (TensorFlow/PyTorch) Experience with large-scale genomic/proteomic datasets and machine learning applied to biological sequences Knowledge of phylogenetics, protein