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University of North Dakota
  • Employee Resources
    • Job Openings
    • Benefits
    • Equal Opportunity
    • Staff Senate
    • TTaDA (Professional Development)
    • UCLC (Childcare)
    • University Council for Women+
    • University Senate
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    • Shared Service Center
    • Grants & Contracts Accounting
    • Procurement & Payment Services
    • Resource Planning & Allocation
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    • Accounting Services-Controller
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    • Policy Office
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  • Robert Chance

Robert Chance

  • Part-time Instructional, Geology & Geological Eng

Contact Info

  • Email: robert.chance@UND.edu

Office Address

Leonard Hall
81 Cornell Street Stop 8358
Grand Forks, ND 58202-8358

Biography

Rob Chance is an early career research scientist who has published papers on using applied machine learning and statistical techniques to model the evolution of arctic environments. Beyond publishing journal papers, Rob has presented at American Geophysical Union, Geological Society of America, and European Geophysical Union conferences. His current research project examines the transport rates of sediment grains smaller than 2 centimeters on western North American hillslopes to understand how these transport rates influence the overall landform system. In addition to geophysical research Rob conducts meteorlogical research on mesoscale systems to understand what atmospheric conditions lead to the most violent meteorlogical hazards.

Rob's teaching duties include GEOL 311- Geomorphology where he lectures student's on processes that shape Earth's dynamic surface and GEOL 101 - Physical Geology where he provides introductory lectures on mineralogy, petrology, sedimentology, structure geology, geophysics, and geomorphology.

Outside of academia, Rob has volunteered with Dakota Venture Group, the first student-run venture capital firm. In this role, he serves as a technical specialist, conducting investment due diligence for startups in sustainability, energy, and AI/automation. Additionally, he manages the firm's daily operations. Rob has also independently consulted for a carbon credit start up and a local bike-share program.

Applied Machine learning

Sediment transport

Arctic systems

Mesoscale systems

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