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    UCSD-SDSU 联合博士项目招生

    C斯达克
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      Oliverpoc 最后由 编辑

      Job description
      We provide the opportunity for the joint Ph.D. program between UCSD and SDSU.

      Research motivation
      Do you know that measurement in a turbulent environment has its own "sixth
      sense"? In fluid dynamical systems, measurements can be used to figure out
      events that happened far away from the probing location.
      Through state-of-art data assimilation techniques, we can trace back the origin
      of any information we have measured. We used this technique to locate the
      release of a pollution release, and reconstruct unknown flow fields from limited
      measurements.

      Are you self-motivated to do a Ph.D. in interdisciplinary researches about fluid
      dynamics, inverse problems, and optimization? We are looking for Ph.D. students
      that are willing to spend time studying in an encouraging and creative
      environment!

      There has been a long-hovering question about how to combine experimental
      measurements with numerical simulations. Especially in terms of designing a
      turbulence model that agrees with experimental studies. In addition, the design
      of sensor networks or sensor weighting can be optimized in terms of the amount
      of information obtained.
      In this Ph.D. project, you will develop novel simulation techniques that combine
      machine learning techniques with data assimilation.

      The required skills and preferred profile
      We are looking for self-motivated young researchers from mechanical
      engineering, aerospace engineering, computational physics, applied mathematics,
      or other closely related areas.

      1. Familiar with MATLAB and FORTRAN with MPI.
      2. C++ and python is a plus.
      3. Good conceptual understanding of calculus and linear algebra.
      4. Experience with simple machine learning algorithms.
      5. Good communication skills including presentation skills, academic writing
        with latex or word.
      6. Please note that the GRE is required for all JDP applicants and cannot be
        waived.
      7. Having a part-time hobby is a plus.

      Location
      Work is carried out in the Data Assimilation group at Aerospace Engineering, San Diego State University.
      We study various inverse problems in fluid dynamics using numerical simulations
      with the discrete adjoint operator.
      For further information, please visit us at
      https://qiwang.sdsu.edu/

      Information and application
      Interested applicants should visit
      https://www.engineering.sdsu.edu/admissions/jointdoc_areomech.aspx
      for more details about applying for the joint program.
      Meanwhile, please reach out to Qi Wang (qwang4@sdsu.edu), including:
      • A short description of your qualifications and motivation to apply for this
      position.
      • CV or resume.
      • Transcripts from your Bachelor and Master degrees.
      Selected candidates will be invited to an interview and should prepare a
      scientific presentation as part of the requirements.

      We highly value diversity at our university. Applicants from all backgrounds are
      welcomed.

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