Opportunity: InnoSpace Masters - International Innovation Competition in Space Applications (Deadline extended!)

The InnoSpace Masters competition (https://innospace-masters.de/) is an international innovation programme that supports the development of novel ideas leveraging space technologies and data. The competition brings together space agencies, industry leaders, and research institutions to identify and accelerate high-impact solutions with both space and terrestrial applications.

Winners and shortlisted participants gain access to:

  • Funding and prize packages (depending on the challenge track)

  • Mentorship from ESA, industry partners, and research organisations

  • Opportunities for further development, visibility, and potential commercialisation pathways

If you are currently working on:

  • AI-driven health insights from spaceflight data

  • Countermeasure development for astronaut health

  • Technologies with dual-use (space and terrestrial healthcare)

…it may be well worth considering a submission.

The deadline has just been extended to 31st March!

Applications are accepted from universities, research institutions, industry, and individuals.

@HUMANawg @MicrobesAWG @AIMLawg

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There is a final Q&A session on Monday 23rd March at 3pm CET

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This challenge on agrifood systems (https://innospace-masters.de/challenges/#ESABAChallenge) might particularly interest the plant guys @PlantAWG

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Hi Sam,

Will Submit

Regards

Sanjay Ektate

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I attached a promotional image that was required as part of the application:

It’s a great opportunity, but I’d also like to remind you that I can apply this to any existing dataset of electroencephalograms. I recall that in a meeting where the book “Neuroscience Research in Short-Duration Human Spaceflight” was presented, it was mentioned that they might share some EEG data they have, although I don’t think that will happen anytime soon. In any case, I’m also leaving a previous message here on the subject in case you ever find it useful:

I will soon share another post about an application of my method for predicting performance in mental arithmetic with very high accuracy, in fact, I have already shared with you the results regarding diagnosis in mental pathologies:

I usually share only publications, but essentially I can find almost anything in the brain as long as the EEGs are well labeled. With this, I want to greatly expand the potential of my method. I can do something similar with genes, but the computational cost would be extremely high if I wanted the same level of precision (though it could be worth it). I mention this because I’m not limited to EEG, however, in the case of genes, I wouldn’t want to limit myself only to diagnosis, since I know I can go directly to causality, although it is considerably more difficult.

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