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at unprecedented resolution. The core innovation of your work will be integrating this data to train deep learning models that predict chromatin accessibility and gene expression patterns. These models will
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novel machine learning models—including Physics-Informed Neural Networks (PINNs), variational autoencoders, and geometric deep learning—to fuse multimodal data from diverse experimental probes like Bragg
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Infrastructure Sciences Division. Machine learning (ML), specifically deep learning (DL), has been demonstrated to successfully predict the weather for 1-14 days with skill on par with numerical weather prediction
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-of-the-art methods, datasets, and challenges Proven experience with: Video data processing for learning and inference Deep learning architectures for video analysis Python programming and PyTorch framework
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approaches to remove atmospheric particulate (e.g., PM2.5) pollution. The math-based subgroup focuses on the use of deep learning and generative AI to address critical problems for the electric grid and broad
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to apply. We seek candidates with expertise in some or all the following areas: density functional theory, deep learning, high-throughput simulations, molecular dynamics, and materials chemistry. Strong
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23.02.2026 Application deadline : 30.04.2026 The Autonomous Systems Lab at the University of Tübingen is searching for a Postdoctoral Researcher in machine learning (m/f/d, E13 TV-L, 100%) limited
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foundation-model experiments. Your role We are seeking a highly motivated Postdoctoral Researcher to join the FNR AI-HPC 2025 BRIDGES project GenePPS, which investigates how machine learning can enable
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inventories) with satellite remote sensing data (e.g., spaceborne lidar and/or hyperspectral observations) and apply machine learning and deep learning approaches to address these questions. This position is
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The Center for Nanoscale Materials (CNM) at Argonne National Laboratory seeks an outstanding postdoctoral researcher to advance data-driven, physics-informed AI for microelectronics materials