Press "Enter" to skip to content

Open Positions

We are actively seeking talented individuals to fill multiple openings for postdoctoral fellows and graduate students in Mechanical Engineering (ME), Civil Engineering (CE), and Materials Science, Engineering, and Commercialization (MSEC) Programs:

Core Competencies Expected at GILab:

  1. Hardworking and Self-Motivation: 
    • High level of personal qualities and work ethic, including the ability to persevere, stay motivated, and put in the effort required to achieve goals.
  2. Computational Physics: 
    • Methods: Familiarity with various numerical methods commonly used in computational physics, such as Finite Volume Method (FVM), Finite Element Method (FEM), Lattice Boltzmann Method (LBM), Smoothed Particle Hydrodynamics (SPH), and Discrete Element Method (DEM).
    • Packages: Proficiency in utilizing open-source software packages like OpenFOAM, LIGGGHTS, and Palabos for simulations and analysis.
  3. Scientific Machine Learning: 
    • Methods: Knowledge and experience in applying machine learning techniques to scientific problems, including Physics-informed Neural Networks (PiNN), Physics-enhanced Neural Networks (PeNN), Generative Adversarial Networks (GAN), and Implicit Neural Representation (INR).
    • Packages: Proficiency in working with popular machine learning frameworks such as TensorFlow and PyTorch, which provide a wide range of tools and algorithms for scientific machine learning applications.

Available Postdoc Positions:

  • P1: A postdoc position focusing on particle-laden viscoelastic fluids dynamics with a specific emphasis on polymeric fluids, complex fluids, and rheology. Candidates with strong background in FVM, DEM, and deep learning are encouraged to apply.
  • P2: A postdoc position focusing on multiphase flow in porous media with a specific emphasis on cross-scale modeling of carbon and hydrogen subsurface storage. Candidates with strong background in FVM or LBM are encouraged to apply.
  • P3: A postdoc position focusing on advanced structural health monitoring with a specific emphasis on big data modeling. Candidates with background in self-sensing material, distributed fiber optics, and deep learning are encouraged to apply.

PhD Positions:

  • D1: A fully funded PhD position focusing on scientific machine learning with application to geo-energy systems. Candidates with strong interests in CFD, complex fluids, multiphase flow, and deep learning are encouraged to apply.
  • D2: A fully funded PhD position focusing on big data intelligence and visualization with application to subsurface or climate data. Candidates with strong interests in deep learning and software development are encouraged to apply.

MSc Positions:

  • M1: Several funded MSc positions focusing on computational mechanics and scientific machine learning (e.g., multifunctional materials, geo-energy systems, structural health monitoring, etc.). Candidates with strong interests in numerical modeling and deep learning are encouraged to apply.

Prospective candidates whose skillsets, research experiences, and interests align with the specific focus areas listed for each available position at the Geo-Intelligence Lab are strongly encouraged to apply by submitting the following items to salah.faroughi@txstate.edu

  • Cover letter,
  • Letter of recommendation, and
  • Resume