MSc thesis Internship — RNA Clock for Neuronal Ageing in Microgravity

Location: House of BioHealth, Luxembourg
Type: Full-time MSc thesis internship

Parkinson's will affect 25 million people by 2050, and there is still no treatment against the disease. The bottleneck isn't chemistry – it's biology. While Parkinson's is a disease of ageing, every lab-grown neuron used to hunt for drugs against it is, in effect, a newborn cell: reprogramming wipes out the marks of age. Drugs are tested on tissue that has never grown old, so they fail in patients.

Microgravity changes that. In orbit, human neurons mature and acquire ageing hallmarks in weeks rather than months. Exobiosphere – the world's first Space Contract Research Organization – is developing a model that will harness this unique advantage. But a model of accelerated ageing needs a way to measure age. That's your project: an RNA clock for iPSC-derived neuronal organoids. Apply existing models or train a new one to answer the key question – how much time does a week in microgravity buy?

You bring: bioinformatics or systems biology, Python/R, and an appetite for stem cells, machine learning, and space.

Responsibilities

• Apply existing transcriptomic ageing clocks to iPSC-derived neuronal organoid data, and train a new model where existing ones fall short.

• Curate and process public RNA-seq datasets covering neuronal ageing, differentiation, and iPSC reprogramming.

• Benchmark model performance and quantify the biological age gained per week in microgravity.

• Build reproducible analysis pipelines with clear documentation.

• Develop graphical representations and data visualizations to communicate results to the team and to partners.

• Contribute to a publication, internal technical report, or website content showcasing the findings.

Requirements

• Currently enrolled in a master's program in bioinformatics, computational biology, systems biology, or a related field.

• Experience in Python and/or R, with hands-on experience handling omics data.

• Familiarity with machine learning fundamentals — regression, regularization, cross-validation, and the difference between a good score and a good model.

• Ability to work with imperfect public datasets and to synthesize complex results into clear, engaging formats.

• Interest in stem cell biology, ageing, and space research.

• Ability to work independently while maintaining regular communication with the team.

Nice to have

• Familiarity with epigenetic or transcriptomic clock literature (Horvath, Peters, and successors).

• Experience with neuronal or organoid models.

Work at the House of BioHealth in Luxembourg.