Research
MNEOS research runs on three fronts: brain health through Neuroclearance, AI that remembers through DOS, and physical engineering where ideas meet hardware. Every program shows exactly where it stands and the next problem you could help solve.
Programs
Brain health and healthy aging
Can a better understanding of brain physiology lead to useful approaches to brain health, recovery and healthy aging?
We are building the instruments to understand how the brain clears, repairs and sustains itself, so that understanding can become better lives. The first bench prototype is being built now.
Scientific AI and memory
Can a research system answer what changed, show where that answer came from and preserve what a human decided to do about it?
AI that remembers why. We are building DOS, a memory that keeps every source, decision and lesson, so human and machine intelligence can compound across every project instead of starting over.
Materials, manufacturing and testing
How should measured results change a model, a material choice and the next experiment?
Ideas have to survive the physical world. Through related programs in materials, manufacturing and testing, we put models and AI to work on real hardware, then build the embodied systems that will do it alongside us.
Open problems
Open problem OP-01
Which measurement limitations could invalidate an interpretation of the first Neuroclearance bench results?
First useful contribution: A proposed calibration, timing and uncertainty plan for the bench configuration, written against the stated conditions.
Open problem OP-02
What would distinguish a transient physiological response from meaningful, durable function in brain-health research?
First useful contribution: An endpoint and alternative-explanations review: what would count, what could mislead, and what a later study would need.
Open problem OP-03
How can a research system retain why a decision was made and revise that record correctly when new evidence arrives?
First useful contribution: A small evaluation set: questions, the sources that answer them, and decision revisions a system should reproduce.
Open problem OP-04
How should measured results change a model and the next experiment, in a way a later engineer can audit?
First useful contribution: A reproducible example with model assumptions, a measurement, comparison criteria and the resulting revision.
Foundations
Foundations
Modeling, optimization and inverse design, uncertainty, geometry and computation, and scientific machine reasoning: mathematics as an engineering instrument.
Foundations
Physics-based modeling, verification, uncertainty quantification and the coupling of models to measured evidence.
Examples on the depth pages that reference specific programs are labeled as illustrative where measured evidence has not been released.
Where this goes
Where the work goes next. Each of these becomes a full program as it gains its own work, evidence and owner, and the people who join early will help decide the order.
