PhD Fellowship
Root Access
| Company | Root Access |
| Category | Science & Research |
| Location | New York City |
| Remote | On-site (inferred) |
| Employment | Not stated |
| Level | Entry |
| Salary | Not stated by the employer |
| Posted | 15 Jul 2026 |
| Last verified | 30 Jul 2026 |
| Source | Employer career page (ashby) |
Description
ABOUT THE COMPANY:
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
You found the opening. Now track it.Tracker, radar and AI drafts in one place.erioun.com →