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the guidance of Dr. Arash Adel, Assistant Professor in the School of Architecture and Associated Faculty of the Department of Computer Science. The desired start date is Spring 2025. Appointments are for one
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or observational cosmology -ML architecture design and deployment experience -High-performance computing and large-scale data analysis -Background in neural density estimation or advanced ML techniques Position
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, working under the guidance of Dr. Arash Adel, Assistant Professor in the School of Architecture and Associated Faculty of the Department of Computer Science. The desired start date is Spring 2025
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-based models, and remote sensing technologies. Required Qualifications • Ph.D. in Civil or Environmental Engineering, Hydrology, Data Science, Geosciences, Computer Science, or a related field. PhD must
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, programming languages, or architecture. This is an open research area position, and the department welcomes applicants from all areas of computer science & engineering who demonstrate strong potential
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 17 hours ago
with Computer Vision and Deep Learning architectures (CNNs, RNNs, etc). Strong foundation in machine learning and data science. Programming skills with proficiency in Python, Java, and/or R. Preferred
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fully funded postdoctoral associate position in bioinformatics and computational biology. This position will be jointly mentored by two investigators including, Dr. Li Chen (onsite) in the Department
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develop the first fault-tolerant quantum computers that can handle applications at realistic scales. To date, several architectures for quantum hardware have been developed that all have their strengths and
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of this position is to conduct research regarding the molecular architecture of the HIV virus in the group of Prof. Juan R. Perilla (http://biophysics.chem.udel.edu ) and the computational core of the Pittsburgh
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scientific facilities: The CFN conducts research on nanomaterial synthesis by assembly designing precise architectures with targeted functionality by organizing nanoscale components. The CFN researches and