Root Access is a frontier electronics company. We are a NYC-based startup funded by top investors. Our team is a passionate mix of engineers across electrical, firmware, software, and machine learning.
About the Fellowship:This is a unique opportunity to lead research on physics-informed neural networks. You will work alongside our team as a member of technical staff during the fellowship to conduct research, exploring challenging scientific questions, and help inform our approach to advancing the field of electronics.
Who this is for:If you are currently pursuing or recently earned a PhD in Math, Physics, Electrical Engineering, or Machine Learning this Fellowship is for you. The fellowship is on-site only in NYC. Fellows are encouraged to go full-time with us during the fall 2026 season but we are open to discussing part-time depending on your needs.
What you will do:As a PhD Fellow conducting research, you'll work alongside the team to develop new approaches to artificial intelligence in the field of electronics.
Projects may include:
Designing experiments and benchmarking methodologies
Developing novel machine learning architectures
Creating differentiable simulation pipelines
Designing new electronics and collecting test measurements
Building large-scale datasets for scientific reasoning
Investigating new approaches to representation learning for engineering
Conducting literature reviews and proposing original research directions
Training and evaluating research models
Publishing internal research reports and, where appropriate, academic papers
Skills Required
- Currently pursuing or recently earned a PhD in Math, Physics, Electrical Engineering, or Machine Learning
- On-site work in New York City (NYC)
- Ability to lead research on physics-informed neural networks
- Experience developing novel machine learning architectures and training/evaluating research models
- Experience creating differentiable simulation pipelines and scientific simulation workflows
- Experience designing electronics experiments and collecting test measurements
- Experience building large-scale datasets for scientific reasoning and representation learning
- Track record or ability to publish research reports and academic papers
- Availability or openness to transition to full-time in Fall 2026 (part-time may be discussed)
What We Do
AI-native developer platform for programming hardware.
