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Field
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performing educational research (mix-methods, surveys). Your primary tasks will be to: Implement full-stack applications, including an AI model in the back-end and GUI (in HTML); Deploy AI models both locally
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state-of-the art methods. Extensive experience in Heterogeneous Catalysis research area as evidenced through peer-reviewed journal publications. Applicants are encouraged to add their three best
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essentially new methods to automatically verify cyber-physical systems. Because none of the existing methods and tools for the formal verification of cyber-physical systems are fully automatic, these methods
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data analysis methods. The PDR will work in close collaboration with LCLS-II staff and eXn graduate students. Experimental results will be interpreted in an interdisciplinary environment including
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methods (e.g., community-based participatory research, interviews, focus groups, photovoice). Skilled in qualitative data analysis and community-based approaches. Demonstrated commitment and ability
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documented by a PhD dissertation and/or research publications a solid theoretical and analytical understanding of text culture, AI, and related fields experience of empirical research methods, such as corpus
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conferences, and engaging in teaching activities. As a formal qualification, you must hold a PhD degree (or equivalent) in either electrochemistry, microbiology, fermentation, or environmental engineering, and
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of solid oxide cell and stack materials Commissioning, modifying, and maintaining testing equipment Developing new mechanical testing methods Preparing samples for mechanical testing, and carrying out post
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aims to identify biomarkers in the eye and brain that explain vision loss, building on our previously-developed method linking clinical, neural and behavioral data (Allen et al., 2018; Miller et al
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of results in scientific journals Requirements PhD in Physics, Engineering, Economics, Environmental Sciences, Mathematics, System Sciences or a related field training in formal, quantitative methods