Work on problems larger than a job description.
MNEOS is beginning to build its founding technical community. Some roles will be conventional employment. Others may begin as consulting, research collaboration, a residency, fellowship, internship, sponsored university work, or project-specific participation. The common requirement is a desire to solve difficult physical-world problems and help build the institution that makes that work possible.
Five paths into MNEOS.
There are five distinct ways to engage with what we are building. Each has its own onboarding path, timeline, and level of commitment. Choose the one that best matches where you are.
Researcher or engineer
You have deep technical expertise and want to contribute to how physics, biology, AI, manufacturing, and sensing operate together. Roles range from consulting to residency to full-time.
Institutional partner
You represent a research institution, university, national lab, or defense agency and see alignment on specific problems. We work through sponsored programs, joint research, and long-horizon collaboration.
Investor or funder
You provide institutional capital for hard-technology institution-building. Qualified parties can request access to protected materials describing the platform, timeline, and capital plan.
Technology or manufacturing partner
Your company builds specialized equipment, materials, sensors, or manufacturing capability. We integrate partner capability into a governed engineering environment.
Student or emerging contributor
You are early in your career, still in school, or transitioning fields. We build fellowship, internship, and sponsored-study paths for people who want to grow into computational-engineering work.
Six domains of active interest.
Physics-first engineering
- Electromagnetics
- Thermal and fluid systems
- Structural mechanics
- Multiphysics
- Numerical methods
- Scientific computing
Systems that reason with evidence
- Scientific machine learning
- AI-agent architecture
- Tool orchestration
- Knowledge systems
- Provenance
- Memory architectures
- Human–AI interfaces
- Evaluation and safety
Physical AI in the laboratory and factory
- Humanoid robotics
- Manipulation
- Controls
- Perception
- Embedded systems
- Autonomous experimentation
- Human-machine teaming
Design connected to making
- Additive manufacturing
- Ceramic systems
- Composites
- Process engineering
- Digital manufacturing
- Qualification and metrology
RF and edge intelligence
- RF and microwave
- Antennas
- Distributed sensing
- Sensor fusion
- Resilient communications
- Edge intelligence
Building the institution itself
- Research-program leadership
- Technical recruiting
- Laboratory operations
- Partnerships
- Scientific communication
- Fundraising and institutional development
Introduce yourself.
MNEOS is assembling its founding community. If any of the domains above align with your work, submit an expression of interest below. Do not submit confidential, proprietary, classified, CUI, ITAR-controlled, EAR-controlled, employer-owned, medical, personal health, or otherwise restricted information.