We are always looking for highly motivated undergraduate and graduate students to join our lab. We are hiring motivated Ph.D. students with diverse backgrounds at Convergent Manufacturing Research Lab (CMRL) at Auburn University for Spring and Fall 2022. Auburn University ranked top 30 public engineering university by U.S. News and World Report’s 2021 (https://eng.auburn.edu/news/2020/09/best-undergraduate-engineering-programs…).
Our convergent research spans mechanical engineering, materials science and engineering, and computer science:
1. Manufacturing Research: Our manufacturing research focuses on metals metamorphic manufacturing (MM), metals additive manufacturing (AM),subtractive manufacturing (SM), and Hybrid Manufacturing. This includes the development of machine learning and deep learning approaches for part and process qualification, optimizing materials for MM and AM, and developing process parameters for new processes and raw materials. We also perform basic research on understanding process-structure-property relationships of MM and AM and how they compare with those of other manufacturing technologies.
2. Materials Research: Our team performs research on engineering new alloys for Additive Manufacturing and Metamorphic Manufacturing. We also perform research on understanding the materials microstructure under various manufacturing processes and offer optimization tools to design desired microstructre.
3. Mechanics Research: We perform basic research on understanding the mechanical behavior of materials under various processing methods. We perform physics-based modeling of structure-property relationships spanning length scales of nanometers to meters. We also develop machine learning, deep learning, and reinforcement learning tools for optimization of processes.
Motivated students with master’s or bachelor’s degree in mechanical engineering, materials science and engineering, and related fields are encouraged to send out the following documents to firstname.lastname@example.org
2. B.Sc. and/or M.Sc. transcripts
3. TOEFL/IELTS score
4. GRE score
5. One published research paper (If applicable)
1. Strong mathematical background
2. Knowledge on Finite Element Modeling and Molecular Dynamics Simulation (e.g. ABAQUS, ANSYS, LAMMPS)
3. Strong knowledge on computational mechanics of materials
4. Strong coding background (C/C++, Python, Fortran)
5. Knowledge on machine learning approaches
6. Hands on Experiments (sample preparation, SEM, EBSD, XRD, tensile test, indentation test, CNC, manufacturing processes)