116 condition-monitoring-machine-learning Postdoctoral positions at University of Washington
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data, research records and materials and other intellectual property generated in University laboratories remain the property of the University. Working Conditions: This position works in a laboratory
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data, research records and materials and other intellectual property generated in University laboratories remain the property of the University. Working Conditions: This position works in a laboratory
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applicants will receive consideration for employment without regard to race, color, creed, religion, national origin, sex, sexual orientation, marital status, pregnancy, genetic information, gender identity
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. Contribute to shared laboratory workspace (ordering reagents and supplies, shared instrument maintenance, etc.). Working Conditions Job Location/Working Conditions Dust, dirt, grease or other disagreeable
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: Information on being a postdoc at WashU in St. Louis can be found at https://postdoc.wustl.edu/prospective-postdocs-2/ . Working Conditions: This position works in a laboratory environment with potential
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laboratories remain the property of the University. Working Conditions: This position works in a laboratory environment with potential exposure to biological and chemical hazards. The individual must be
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). • Familiarity with cloud computing and machine learning techniques. Instructions Applications for this position should include a: (1) Curriculum Vitae with the names and contact information of 3 references (2
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scientific papers, and contribute to the overall preparation of research for publication. To assist in the training of PhD and undergraduate students. Working Conditions: This position works in a laboratory
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vulnerability or survival during neurodegenerative conditions in vivo. Responsibilities of the postdoc will include, but are not limited to: in vivo imaging of biosensors to read out diversity and dynamics
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industrial engineering, systems engineering, computer science, electrical engineering, or a related field. · Strong background in machine learning or data analytics and hands-on experience handling big