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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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independent research and develop novel algorithms. You have strong analytical and problem-solving skills. You have a research-oriented mindset and motivation to work at the intersection of AI and communication
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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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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
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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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of Coimbra III- Scientific supervision/coordination of the grant: Rui Paulo Pinto da Rocha IV - Work Plan / Goals to be achieved: 1. Development of algorithms for swarm robotics and human–swarm interaction 2
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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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Research Infrastructure? No Offer Description Mission: Provide high-level scientific and technical advice for the line of research in Robot Personalization. Functions to be developed: Supervision
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on developing the next generation of approaches for analyzing spatiotemporal data arising from sports matches (e.g., tactcal analyses, player/team evaluation, …). See https://dtai.cs.kuleuven.be/sports 4) 1-2
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learning. Job responsibilities will include: Develop simulation algorithms and software to model challenging gas adsorption behavior in porous materials Develop novel machine learning model for predicting