Computational Biology / Computer Vision Scientist — Medical Deep-Tech

We are a medical deep-tech venture at the final POC stage, preparing for our Seed round.

We have already collected a proprietary longitudinal dataset of real clinical cases containing synchronized:

RGB + 3D/Depth + Thermal + NIR imaging

We are now looking for a hands-on computational scientist / computer-vision researcher who can help us turn this existing data into quantitative biological information.

Raw images/video → quantitative measurements → biological signals → candidate digital biomarkers

We need someone with:

• Technical focus: Multimodal image processing & clinical dataset engineering
• Action focus: Building Python pipelines for quantitative feature extraction
• Result focus: Transforming raw multimodal data into a structured, reproducible dataset and identifying potentially meaningful biological signals

This is an execution role, not a theoretical research exercise.

We are looking for someone who enjoys opening unfamiliar data and figuring out:

Someone with hands-on experience in one or more of:

Computer Vision • Biomedical Imaging • Computational Biology • Bioinformatics • Scientific Computing • Medical AI / ML

Strong Python and image/data-processing skills are important.

M.Sc. / Ph.D. preferred, but demonstrated ability to execute is more important than the title.

1. Immediate project engagement

A hands-on researcher who can join us for a few weeks to approximately one month to help complete this stage of the POC.

2. Long-term opportunity

For a more senior person with the right vision and experience, there is an opportunity to become a long-term scientific/technical leader or potentially Co-founder, leading the computational biology / AI direction as we move into Seed.

The initial engagement can therefore be project-based, with the potential to develop into a longer-term relationship depending on fit and contribution.

We are not simply trying to build another wound-measurement tool.

We are exploring whether multimodal longitudinal tissue observations can reveal biological trajectories and digital biomarkers that are not visible from conventional 2D imaging alone.

The immediate goal is simple:

Take what we already have → extract the information → prove what is useful → move to Seed.

If you are a hands-on scientist who likes difficult data, fast execution and discovering what is hidden inside it, or know someone who is, please DM me.

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