142 engineering-computation "https:" "https:" "https:" "https:" "https:" "https:" "https:" "U.S" Fellowship positions at National University of Singapore
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of Materials Science and Engineering at National University of Singapore (NUS). With a strong focus on industrial projects, we are developing our exciting technologies beyond the lab scale. As a Research Fellow
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Computer Engineering. More information on the laboratory is available at www.neuroimaginglab.org . The MNNDL group at NUS is a multidisciplinary team studying the human neural bases of cognitive functions
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, Atmospheric Sciences, Urban Climate, Architecture Engineering, Building Science, or related fields majoring in Computational Fluids Dynamics (CFD); Well informed and knowledgeable in urban climate modelling
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highly numerate subject (e.g. engineering, mathematics, physics, chemistry, statistics, econometrics, computer science, climate science) is advantageous. For Research Fellow and Senior Research Fellow
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from the Department of Civil and Environmental Engineering. This position is part of an exciting research program advancing separation technologies (i.e., membrane and electrochemical) to solve pressing
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Engineering on leading projects that uses advanced materials for energy and environment related challenges. Qualifications The candidate should have a Ph.D. in Materials Science, Chemistry, Engineering, or a
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Engineering. The Research Fellow is expected to: • Conduct novel and impactful research. • Assist in the design of and lead experimental activities within the lab. • Disseminate research findings
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from the Department of Civil and Environmental Engineering. This position is part of an exciting research programme aimed at advancing the multi-robotic wire-arc directed energy deposition technology and
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focused on immuno-oncology imaging and engineering, nucleic acids nanotechnology, computational biomedical imaging. We are seeking a person with preferred experience in cancer immunology, immune engineering
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science, computational biology, engineering, statistics, mathematics.). Experience and interest in prediction modelling Proficient in statistical software and programming languages and familiarity with relevant libraries