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application! Your work assignments We are looking for a PhD student to work on the development of novel spatio-temporal machine learning methods. Our world is inherently spatio-temporal, i.e. physical processes
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of Geodesy and Earth Observation (GEO). In this position, you will develop algorithms and methods aimed at improving GNSS position integrity and mitigate/reduce both natural and intentional signal interference
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) Ideal candidates have Specialist Knowledge in Neuromorphic Engineering, with experience in designing and testing Mixed Mode IC systems, working with and/or developing neuromorphic hardware in particular
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the racial justice implications of technology and algorithmic decision-making tools in the criminal legal system and other systems that govern people’s lives; challenging the forces that drive racial
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-modal”) neural + behavioral disease-state models. The purpose of the research project(s) this position supports: The purpose of the research supported by this position is to develop a computational
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to contribute to both methodological AI research and applications in fundamental science. In this position, you will have the opportunity to develop and apply new AI algorithms for science. Our group also
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National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | 8 days ago
to): Develop machine learning algorithms that utilize fire products from geostationary satellites to better represent fire evolution and variability Develop machine learning emulators to represent forward
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of digital signal processing algorithms. Other qualifications For the doctoral programme in question, the following are considered as other qualifications: Experience with development tools for digital
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Computational Biology (BiRC), Department of Molecular Biology and Genetics (MBG), Aarhus University (http://birc.au.dk), Denmark. The application deadline is 7 April 2026. The position We seek a highly motivated
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incremental optimization. We seek researchers to develop next-generation machine learning methods that fundamentally rethink how large-scale AI systems are trained, fine-tuned, and deployed. Our focus is on