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National Aeronautics and Space Administration (NASA) | New York City, New York | United States | about 1 month ago
climate system and the effect of ice-atmosphere feedbacks (e.g., ice albedo feedback and cloud feedback) on sea ice evolution. Developing radiative transfer algorithms to improve both the physical
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alternative ways of approaching reconstruction and variability analysis. The project combines applied mathematics, computational imaging, and structural biology. You will develop algorithms, implement and test
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an excellent work ethic and background in molecular simulation and machine learning. Job responsibilities will include: Develop simulation algorithms and software to model challenging gas adsorption behavior in
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passionate about applying ML algorithms and developing AI applied research and innovation solutions using classic ML to novel transformer neural networks. We test and measure the real customer impact of each
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EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Are you passionate about mathematical systems and control theory? Do you want to develop cutting
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School. Responsibilities will span all stages of research, including collecting data of in both tabular and spatial formats, developing algorithms that clean and organize data, conducting statistical
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electrical stresses. Specific goals include: - Development of a hybrid model combining degradation indicators and AI-based algorithms. - Integration of the model into an online monitoring framework
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postdoctoral researcher to join our dynamic animal science and artificial intelligence team. The candidate will develop novel algorithms that advance the use of computer vision in precision livestock farming
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), and the European Innovation Council (EIC). Project description Superconducting quantum circuits is a pioneering field of research to develop cutting-edge quantum technology, especially quantum
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communications signals. The objective of QUESTING is to develop new methods for quantum networking, fault-tolerant design and resource-efficient hybrid systems by training new generations of Q-System Innovators