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, scale and resolution in which in vivo pathways of immune cells can be unraveled. Furthermore, it provides a goldmine for training causal machine learning models to move towards precision medicine
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experimental design. Collaborate with another postdoc in the NIH Center to use scientific machine learning (SciML) to automatically select mathematical models from data. Minimum Requirements: Ph.D. in applied
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addition to this basic research, FTRG is involved in large community-based prevention efforts in both military and civilian populations. These studies use the full spectrum of research methods, from intensive
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data Proficiency in scientific computing and geospatial data processing Hands-on experience applying statistical and/or machine-learning methods to real geospatial problems, including model validation
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an experimental team, with direct availability of experimental validation for machine learning models. Competitive salary and full benefits. Access to state-of-the-art computing infrastructure. Fully funded for 4
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Research Project“ Transforming Cardiac Research: Visual Exploration and AI Prediction Modeling of Real-Life, Multi-Modal Data” as a PhD-Position in machine learning. You will work alongside leading experts
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locomotion. Apply machine learning and machine vision algorithms to track body and limb movements. Use biomechanical modeling to analyze walking data and fit locomotion models. Operate a force sensor to
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. Knowledge on multiphase (gas-particle two phase system), thermal energy storage, and/or renewable hydrogen technologies. Familiar with application of machine learning and deep learning algorithms to fluid and
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deep learning to solve complex, high-impact problems. The ideal candidate will have a strong grasp of diverse machine learning techniques and a passion for experimenting with model architectures, feature
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machine learning for next-generation wireless networks, (ii) Foundations of semantic communications and age of information, (iii) Stochastic geometry and spatial modeling of large-scale wireless systems