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populations. Apply Artificial Intelligence (AI) methods including deep learning (DL) models and supervised and unsupervised machine learning (ML) methods for integration and for Genome-2-Phenome (G2P) and risk
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. Apply deep learning approaches to support optimization of segmentation methods for clinical neuroimaging datasets. Investigate developmental differences in infant brain functional networks. Support
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learning and deep learning models for trait prediction and climate-resilient wheat breeding. Analyze time-series UAV data using crop models in combination with genomic and agronomic information. Collaborate
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U.S. Department of Energy (DOE) | Washington, District of Columbia | United States | about 2 months ago
, projects, and activities at the Department. Fellows will receive hands-on experience that provides an understanding of the mission, operations, and culture of the DOE. As a result, fellows will gain deep
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, subjective user feedback, and environmental data. The research will involve machine learning, human-centred experimentation, real-time comfort prediction, and the integration of intelligent climate control
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applications for a fully funded postdoctoral associate position. This position, available immediately, focuses on developing machine learning and deep learning methods for analyzing large-scale single-cell DNA
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analysis packages, basic shell scripting, experience in Unix/Linux platform) and experiences with deep learning tools (e.g., PyTorch, TensorFLow, Keras), neuroimaging analysis tools (e.g., PMOD, SPM, FSL
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studies. Strong background in AI/ML fundamentals and extensive experience with deep learning (DL) methods. Demonstrated proficiency in Python and machine learning frameworks (e.g., PyTorch, Jax, scikit
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executed. In close collaboration with PhD researchers and project partners from TUM and ETH, you will contribute to the development of novel control and learning methods for aerial manipulators and multi
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, or a related field. Solid research background and practical experience in one or more of the following areas: Reinforcement Learning / Deep Reinforcement Learning Fine-tuning and Application of Large