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Field
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. Preferred Qualifications • Experience with deep learning architectures applied to geophysical or environmental data. • Familiarity with physics-informed machine learning or hybrid modeling approaches
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Science or related field. Experience in one or more ML domains, such as deep learning, reinforcement learning, or human-centered ML. Proficiency in programming languages (e.g., Python) and ML frameworks (e.g
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Google Earth Engin, R, Python, and STAN (e.g., deep learning, Bayesian regression models, spatial analyses), and running analyses on a high-performance computing cluster. Demonstrated record of publishing
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) analysis • Research, development and implementation of deep-learning approaches • Network architecture search • Real-time image analysis • Establishing multi modal (video, thermography, acoustic, RFID
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experience: Deep Reinforcement Learning, DNNs/CNNs Federated and Decentralized Learning (Split Learning, Swarm Learning, peer-to-peer FL) Fine-tuning FMs/LLMs for domain-specific applications Generative AI
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deep learning / computer vision / biomedical image computing Excellent English; German is an advantage for clinical collaboration We offer Highly interdisciplinary, translational research environment
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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