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Previous Job Job Title Dunham-Jackson Postdoctoral Associate - School of Mathematics Next Job Apply for Job Job ID 370276 Location Twin Cities Job Family Academic Full/Part Time Full-Time Regular
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on Accreditation in Clinical Chemistry (COMACC). This is a two-year program for postdocs or one-year program for MDs. The Program’s principal objective is to educate, train, and prepare clinical chemists to provide
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of appointment. Specific research areas of study include high energy phenomenology, field theory, astroparticle physics and cosmology. About the Department School of Physics and Astronomy: https://cse.umn.edu
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supervision of Dr. Anita Schuchardt to conduct an NSF funded research project related to instructors’ and students’ sensemaking of mathematics in sciences. In general, the post-doctoral researcher will be
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protocols, on both a day-to-day and long-term basis. Design new experimental protocols based on literature and lab discussion and develop experimental plans to validate and apply the protocols. 5%, Prepare
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dissertation project on or substantial publication in some topic in philosophy of science. Scholars working in all areas of philosophy of science, inclusive of philosophical approaches to logic and mathematics
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. Our research investigates how receptor-mediated signal transduction is regulated in time and space within cells. As a postdoctoral associate, you would spearhead a project related to these themes while
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science, engineering, linguistics, mathematics, medicine and allied disciplines, philosophy, physics, and psychology. The Department of Neuroscience is committed to providing excellence in research, education, and
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research areas include Biomedical applications, Communication systems and information theory, Digital systems and computer architecture, Microelectronics, Micro-electromechanical systems (MEMS), Nano
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computational modeling of behavior to identify the underlying circuit computations. Current projects in our laboratory emphasize task-structured behavior assays, including set shifting, reversal learning