About the position
The Computational Design (CD) group of the AI Center at SRI International is seeking an enthusiastic Master's or Ph.D. student in engineering, physical, or computational sciences to join our team as a Research Intern for Summer 2025 (full-time), potentially extendable for up to a year (full- or part-time). The Intern will work on-site at our Menlo Park, CA office. Founded in 1966, SRI's Artificial Intelligence Center (AIC) has played a key role in AI research since the development of Shakey the Robot in the early days of the field. The lab continues to push the boundaries in AI as it develops innovative technologies and explores their application across a range of domains. The lab has an outstanding track record of moving its research into the real world with successes such as Siri (developed originally on DARPA funding, subsequently acquired by Apple) and the BioCyc family of bioinformatics knowledge bases and tools which have been licensed by over 11,000 users. The Computational Design (CD) group was founded in 2020 in the iconic Palo Alto Research Center (PARC) that merged with SRI in 2023. CD has been at the forefront of applying computational and AI methods to democratize design for heterogeneous constraints such as physics-based performance, manufacturing readiness, and supply chain resilience. The intern will work on an exciting ongoing project funded by the Defense Advanced Research Projects Agency (DARPA) on design and digital manufacturing, particularly on co-design of engineering system components and materials for high-end applications in aerospace, energy, and consumer products.
Responsibilities
- Contributing to existing research projects through developing solutions and algorithms.
- Implementing software code and databases.
- Evaluating results and contributing to conference presentations, journal publications, and invention disclosures.
- Contributing to gradient-descent optimization algorithms to design shape, topology, and material layout in three-dimensional (3D) domains.
- Contributing to physics-based and data-driven models to evaluate coupled mechanical, thermal, and other physical performance criteria, manufacturability constraints, and other requirements.
- Using and integrating open-source numerical simulation libraries (e.g., MFEM, FEniCS, or Moose), commercial software APIs (e.g., Ansys and Abaqus), material modeling tools and databases (e.g., ThermoCalc and JMatPro) and other off-the-shelf computational packages as needed.
Requirements
- Currently enrolled in a Master's or Ph.D. degree program in Mechanical Engineering, Electrical Engineering, Physics, Applied Math, Material Science, Computer Science, or related discipline.
- At least two years of academic research experience as an undergraduate or graduate student.
- Strong mathematical and computational foundations, especially geometric and physical modeling.
- A background in computational physics (e.g., solid/fluid mechanics), computational geometry, computational materials, or related research areas.
- Familiarity with optimization techniques, numerical analysis, and statistical learning (e.g., AI/ML).
- Familiarity with at least one programming language (e.g., Python or C++) and one ML model development framework (e.g., PyTorch, JAX, or TensorFlow).
- Familiarity (or ability to quickly learn) open-source libraries or commercial software APIs for physics-based numerical analysis, optimization, and AI/ML.
- Demonstrated experience with research-grade publications as a first author in high-quality peer-reviewed journals or conferences.
Nice-to-haves
- A working knowledge of programming styles (e.g., object-oriented programming) and best practices to enable code readability and maintainability, version control and collaboration, testing and debugging, etc.
- Related industrial or entrepreneurial experience (including internships).
- Experience with manufacturing and testing techniques.
- Experience with Product Lifecycle Management (PLM).
Benefits
- Salary range of $40-$45/hr.
- Potential qualification for SRI's Pay for Outstanding Performance program or the annual Performance Based Compensation program.
- Competitive benefits package.
