data analysismachine learningfluid dynamicshigh performance computingscientific computingcomputational fluid dynamicsparticle trackingturbulence
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Это четко структурированная исследовательская позиция в ведущем аэрокосмическом департаменте с отличными возможностями для развития и солидным соцпакетом.
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Hirify AI/Оригинал
TL;DR
Postdoctoral Researcher (Computational Fluid Dynamics): Conducting high-fidelity simulations of wall-bounded flows, separated flows, wakes, and airfoils with an accent on turbulence, computational methods, and data-driven modeling. Focus on developing and validating CFD tools, analyzing large-scale simulation data with high-performance computing, and studying complex flow phenomena including separation, relaminarization, and wake dynamics.
Location: Main Campus, College Station, Texas, United States
Company
Texas A&M Engineering, part of the Texas A&M University System, conducts basic and applied engineering research, workforce development, and technology transition.
What you will do
Conduct research in fluid dynamics, turbulence, and computational fluid dynamics.
Develop and perform high-fidelity simulations of boundary layers, separated flows, wakes, and airfoils.
Develop and validate computational methods, including particle-tracking and flow-analysis tools.
Use high-performance computing for large-scale simulations and data analysis.
Analyze turbulence, flow separation, relaminarization, and wake behavior to support physics-based and data-driven models.
Collaborate with faculty, students, and external research partners; prepare technical reports, conference presentations, publications, and mentor graduate and undergraduate students.
Requirements
Doctorate degree required.
Strong background in fluid dynamics, turbulence, computational fluid dynamics, and scientific computing.
Experience with high-fidelity simulation methods such as DNS and/or spectral or high-order numerical methods.
Experience using high-performance computing systems.
Experience with Lagrangian particle tracking and Eulerian-Lagrangian data analysis.
Experience with wall-bounded turbulence, pressure-gradient flows, flow separation, or wake dynamics.
Strong written and verbal communication skills.
Nice to have
Ph.D. in Aerospace Engineering, Mechanical Engineering, Applied Mathematics, Physics, or a related field.
Familiarity with reduced-order modeling, surrogate modeling, or physics-informed machine learning.
Culture & Benefits
Full-time staff position in a multidisciplinary, multi-university research environment.
Medical, prescription, vision, dental, life, and long-term disability insurance options.
Defined-benefit retirement plan with an 8.25% employer contribution, plus voluntary retirement programs.
Paid holidays, vacation and sick leave, flexible spending accounts, wellness programs, and work-life balance support.
Training through LinkedIn Learning, professional development opportunities, tuition assistance, and educational release time.
Employment requires completion of the federal I-9 process and proof of authorization to work in the United States. Positions are security-sensitive and subject to a criminal history investigation and credential verification.
Hiring process
Submit a resume or CV, three work references with contact information including at least one supervisor or former supervisor, and several representative publications.
All required job application data must be entered; incomplete submissions may be rejected as invalid.
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