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and kinetic modeling. A PhD in biomedical engineering, physics, or a related field is required. However, interested candidates with a strong computational background and interest in getting involved in
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Academic credentials: PhD in ecology, biology, statistics, computer science or engineering, or oceanography with a strong quantitative analysis background, particularly in species distribution modeling
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Uppsala University, Disciplinary Domain of Science and Technology, Faculty of Mathematics and Computer Science, Department of Information Technology Are you interested in working with probabilistic
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students and fellow postdoctoral researchers within the laboratory and across EPFL’s international research network. Profile PhD degree in environmental engineering, microbiology, chemical engineering, or a
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Post-Doctoral Associate in the Division of Engineering (Mechanical Engineering) - Dr. Mohammed Daqaq
interactions. Applicants must hold a Ph.D. in Mechanical Engineering or a closely related discipline, with no more than five years post receipt of the PhD. Strong English communication skills, the ability
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: Polymer synthesis/modifications Human cell culture Biological characterizations/assays (e.g. qRT-PCR, ELISA, confocal microscopy) Preferred Qualifications: PhD in Chemical Engineering or Bioengineering
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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | 20 days ago
ocean models, available in-situ data, and satellite observations. The study builds on analyses from a consortium of models, such as Estimating the Circulation and Climate of the Ocean (ECCO, http
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requirements: PhD obtained less than seven years ago in Chemistry, Materials Science/Engineering, Chemical or Biomedical Engineering, Biotechnology, or a related field. Proven experience in electroanalysis and
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, please submit your CV along with a separate document responding to the selection criteria below. Essential A PhD in mechanical/mechatronics/materials engineering or a closely related discipline
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of Technology. The NTO works closely with tutoring providers, school and district leaders, and education researchers to create the world’s largest open-access dataset of tutoring (the Million Tutoring Moves [MTM