Postdoctoral Research Associate – University of North Carolina at Chapel Hill

Postdoctoral Research Associate for laboratory and numerical study of stratified turbulence at oceanic scales

The Marine Sciences Department at the University of North Carolina at Chapel Hill (UNC) is seeking a Postdoctoral researcher as part of the NSF funded LABMOST project (a LABoratory for Mixing in Ocean- scale Stratified Turbulence). The proposal leverages the large wave tank in the Joint Applied Mathematics and Marine Sciences (JAMMS) laboratory to make fundamental measurements of stratified turbulence to improve mixing parameterizations in ocean models. The project will include experimental measurement of turbulent fluxes with a variety of instruments (PIV, LIF, fast Conductivity- Temperature) and use of the data to train numerical models with data assimilation and/or machine learning methods. Applicants with both numerical and experimental fluids experience and with an emphasis on data-driven/machine learning methods are particularly encouraged to apply.

This is a recruitment for one 24-month, full-time (100% FTE) appointment. In addition to his primary duties, the candidate will have the opportunity to interact with the other members of the
JAMMS laboratory, and will have access to the UNC Supercomputing center. Applicants must provide proof of the PhD conferral prior to start. A summer 2019 start date is anticipated.

Interested candidates are encouraged to submit an email with a cover letter, current CV (including date of Ph.D.), and a one page statement of research interests and experience addressed to both Brian White (bwhite at unc dot edu) and Alberto Scotti (ascotti at unc dot edu). We particularly encourage applications from women and people from underrepresented groups.

Ideal Qualifications:

  • PhD in Physical Oceanography, Engineering, Applied Math or related fields.
  • Strong fluid dynamics background
  • Experience with experimental measurement of fluids (PIV, LIF)
  • Experience with data assimilation / data-driven / machine learning methods in fluids (e.g. Kalman filter, optimal control, Dynamic Mode Decomposition, etc.)
  • Experience with numerical methods / Computational Fluid Dynamics

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