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will be involved in quantum sensing experiments (temperature/magnetic field) that may involve novel materials such as point defects in 2D materials (e.g., in hexagonal Boron Nitride and related) and
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Postdoctoral Scholar – Research Associate to join the laboratory of Dr. Yang Chai (https://chailab.usc.edu/ (opens in new window) ). The Chai Lab is interested in the regulation of developmental patterning
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, with flexible start dates. As a TRAILS Postdoctoral Associate, you will have the opportunity to pursue your own research agenda, interact with our Institute’s diverse array of researchers, and contribute
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an interdisciplinary research team developing an AI-enabled framework that translates high-level mission objectives into deployable communications signal-processing (DSP) solutions. The role combines
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submitted in this way will not be included in the selection process. Where to apply Website https://seleccio.uab.cat/ Requirements Research FieldHistory » PrehistoryEducation LevelPhD or equivalent Research
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fingerprinting (RFF) systems using machine learning. The successful candidate will have significant experience in signal processing or machine learning, and an outstanding track record in conducting research. You
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of detailed curriculum vitae structured according to the items under point 5; c) Other documents that are relevant for the evaluation of the qualification in related scientific areas; d) Any other documents
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written Communication Skills. Certificates/Credentials/Licenses M.D. or PhD Required Computer Skills General office suite, GraphPad-Prism, Endnote, Snapgene, and willingness to learn lab specific software
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this knowledge. In previous roles you have worked with the application of data science and artificial intelligence in the context of biomedical signals. You enjoy the process of conceiving and writing papers (in
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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | 11 days ago
inform representations and encoder adaptations. This activity will provide hands-on experience in making three-dimensional vegetation structure accessible within data-driven geospatial modeling processes