Fully Funded PhD Thesis – FPGAs for AI



Position Type

Full Time

Job Description

Rapid Silicon is committed to advancing research and is opening a fully-funded PhD topic in collaboration with the University of Utah. This position entails exploration of advanced FPGA-based hardware acceleration techniques, develop innovative FPGA architectures suited to AI workloads and will develop a novel framework for for FPGAs to easy support current and emerging AI applications.

Minimum Qualifications

  • MSc in Computer Engineering / Computer Science with very good grades.
  • Eligible to be enrolled at The University of Utah in the field of engineering (e.g., computer science, electrical engineering, computer engineering)

Preferred Skills

  • Practical experience in deep learning, machine vision and/or machine learning
  • Good knowledge of a deep learning framework (e.g., TensorFlow, Keras, PyTorch)
  • Practical experience with FPGAs
  • Very good knowledge in Python and Verilog

Deadline to Apply

Drop us an email, with attached resume, to careers@rapidsilicon.com
*We’re an equal opportunity employer. All applicants will be considered for employment without attention to race, color, religion, sex, sexual orientation, gender identity, national origin, veteran or disability status