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Field
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Postdoctoral Researcher in Machine Learning of Isomerization in Porous Molecular Framework Materials
broad range of applications. Computational chemistry and Machine Learning increasingly underlies MFM research to search or screen candidate MFMs prior to synthesis. A major drawback when applying
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Postdoctoral Associate Required Qualifications: (as evidenced by an attached resume) PhD (or foreign equivalent) in Physics, Electrical Engineering, Material Science or closely related field in hand
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Institute faculty, in areas such as: * Machine Learning and Computer Vision * Natural Language Processing and Data Science * Biomedical Informatics and Computational Neuroscience * Mathematical/Theoretical
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Biomedical Science related Master´s degree or DVM degree in addition to a PhD are preferred. This position will require attention to detail, leadership abilities, problem-solving skills, and a passion for
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through soft, disordered materials, including auto-regulated networks, composite soft solids, and exotic photonic biomaterials. The lab has two fully funded PhD and/or postdoctoral positions available
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technologies, in coordination with academic and industrial collaborators. Qualifications Applicants must hold a PhD degree in electrical/electronics engineering, telecommunications or related field. Other
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degree or DVM degree in addition to a PhD are preferred. This position will require attention to detail, leadership abilities, problem-solving skills, and a passion for hands-on work, and caring
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Postdoctoral Associate Required Qualifications: (as evidenced by an attached resume) PhD (or foreign equivalent) in Physics, Electrical Engineering, Material Science or closely related field in hand
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on reliability, security, and resilience of electric power systems and microgrids and stability analysis and Scientific Machine Learning (SciML) for microgrid applications. The successful candidate will be
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You have academic qualifications at PhD level, for example within the areas of bioinformatics, machine learning or forensic odontology. We favour experience in computational data analysis, and the