Intern - Microelectronics Algorithms, Software Scaling

Stanford University is offering a summer internship focused on estimating scaling of algorithms and software used in Machine Learning and Scientific Applications. The intern will analyze various ML and AI software, conduct data analysis, and develop models related to energy efficiency in computing, while gaining hands-on experience in real-world scientific applications.

What you'll do:

• Identify the different algorithms and software including those ported to CPUS, GPUS, and Application Specific ICs and new customizable architectures
• Develop metrics for measuring algorithmic scaling and complexity
• Outline the nature and quantity of data required for each of the hardware, in discussion with the mentor
• Conduct data analysis to ascertain the quality and verity of data, in partnership with the mentor
• Develop models for estimating energy estimates for the problems in working with the mentor
• Carry out Verification, Validation and critical analysis of their estimates
• Refine and develop models to integrate scientific knowledge in the model formulation and training phases
• Prepare reports and scientific publications outlining the advances under the aegis of the mentor

What they're looking for:

• Pursuing a Master's or Doctoral degree in a science, engineering or equivalent discipline
• Strong communication skills
• Ability to work in a collaborative environment
• Passionate about innovative solutions for Science & Engineering problems
• Eligible applicants must be at least 18 years of age
• Currently enrolled in an educational program or recently graduated
• Have US work authorization
• Some prior experience at Python programming would be beneficial

Benefits:

• Growth and mentorship from exceptionally talented engineers and scientists from SLAC and Stanford University.
• A mission-driven, stable, collaborative, highly interdisciplinary, and supportive work environment.
• Opportunity to experience a multidisciplinary research environment, integrating knowledge from many subject areas spanning computer engineering, physical sciences, applied mathematics, and software applications.