Looking for Collaborators-Building AI That Keeps Space Sensors Honest

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Hi everyone!
I’m looking for a small number of collaborators for a research project on AI-based early detection of drift and sensor degradation in space environments. The project involves areas such as sensor modeling, ML/AI, space systems, or data analysis — depending on your background.

Because the project will be developed with support from Imperial College for testing, we’re keeping the team compact and focused.

More about the project:

AI Integrated Sensor Drift Prediction

We aim to teach sensors to tell the truth even when space tries to break them.

Space sensors drift due to radiation, temperature swings, vibration, and aging.

Even tiny drift threatens navigation and mission safety.

What is Sensor Drift?

A sensor should read 25°C**,** but begins reporting 28°C after months in orbit.

Small deviation → large mission risk.

Our Research Focus

AI to detect early drift, predict failure, and auto-correct sensor readings in real time or send alerts about the risk.

If you’re interested, you can either:

  • Reply directly to this thread with a short description of your background/skills, or

  • Email me a brief note (CV/resume optional):
    :envelope_with_arrow: elssuna123@gmail.com

This helps us match people to the right roles.
I’m also happy to share more details or answer questions! I’m open to any suggestions and comments.

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Hi, I would be interested if there was any way I could contribute. My background is more based in person centred quality and risk / incident prediction and management however. What I find fascinating in your project is how / if we can reliably tell early sensor drift or early failure warning from a genuine but unusual truth. I guess that that diagnostic boundary is more where my background fits.”

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Thanks for your message! I’d be happy to have a quick chat to see how your experience could align with this project.

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Hi. I,m happy to catch up if you would like.
I would be particularly interested in how one could separate the four key cases — genuine environmental truth, slow drift, emerging degradation, and the short-term causal anomalies you get from things like radiation hits or power transients, ensuring the AI preserves truth rather than producing stable but misleading outputs.
I’m based in Australia, so a wee bit geographically remote, but remote collaboration is straightforward for me.”