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Field
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. Supervise laboratory interns or students, if there is mutual interest. What is Required: Ph.D. in Geology, mineralogy, earth science, chemistry, materials science, physics, computer science or relevant STEM
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have strong English skills and demonstrate an ability to write and communicate with a scientific audience. Candidates must be able to independently plan, multi-task and complete projects. Ideally
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optical cavities, lasers, or long-range sensing. The Limbach group in the Department of Aerospace Engineering at UM performs research at the intersection of optical science, sensing, and aerospace
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of Engineering and Applied Sciences. The fellow will design and run human experiments, perform data analysis, and create computational models of learning and memory. A PhD is required. An ideal candidate will be
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to the publication of scientific papers and presentation of findings to scholarly meetings and stakeholders. Collaborate and coordinate with research program team members and participate in program development through
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motivated candidates for postdoctoral fellow positions within the Bureau’s hydrology research group. These fellows will be key members of an expanding research program focused on assessing Texas water
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for Science and Education (ORISE), was established through an interagency agreement between DOE and ARS. Participants do not become employees of USDA, ARS, DOE or the program administrator, and there are no
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of intelligence, including mathematical and computational models of intelligence, cognitive theories of intelligence, and the neurobiological basis of intelligence. Research and development of new AI or ML
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send materials to ukarvind@umich.edu with Subject "SIBL Bioinformatics Postdoc". Job Summary The Systems Imaging and Bioinformatics Laboratory (SIBL) in the Department of Computational Medicine and
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Qualifications* PhD Degree in Engineering, Computer Science, Data Science, Applied Mathematics, Statistics, or a related field Familiarity with (biomedical) signal processing Experience working with clinical data