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to contribute to its portfolio of research. The Postdoctoral Research Associate will develop new computational methods in knowledge-grounded artificial intelligence, systems modeling, natural
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. The research focus is on developing and applying first-principles-based and data-driven computational methods to understand multiscale processes and accelerate chemical discoveries for renewable energy
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artificial intelligence (AI) methods that propose, prioritize, and interpret experiments, accelerating the discovery and optimization of catalytic materials for energy and sustainability applications
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genetically diverse mice under a multitude of conditions, and (3) develop multimodal machine learning models and methods to determine signatures and biomarkers to understand mechanisms distinguishing
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) develop multimodal machine learning models and methods to determine signatures and biomarkers to understand mechanisms distinguishing spontaneous versus precipitated withdrawal episodes. The spontaneous vs
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underserved populations. Research skills and experiences will include utilizing mixed methods research, community engagement, qualitative, quantitative, and big data collection and analysis. This position
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. Work will emphasize the development and analysis of advanced methods in areas such as sparse signal recovery, compressed sensing, and statistical estimation, with a particular focus on their role in
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QUALIFICATIONS: • PhD in Electrical and Computer Engineering, Mechanical Engineering, Physics, or a closely related field. • Demonstrated expertise in MEMS/NEMS design and modeling, including finite element
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findings in peer-reviewed publications; Ability to work collaboratively across clinical and engineering teams. Normally acquired through a PhD in rehabilitation science, neuroscience, biomedical engineering
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speaking. Minimum Qualifications A PhD in neuroscience, psychology, or a related field and at least three years of experience in a rodent neuroscience lab is required Skills related to the techniques listed