Data & Analytics Jobs in Petit-Thier, Belgium (Now Hiring) — 7 open

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The intern must be fluent in Python programming and familiar with machine learning tools and frameworks such as scikit-learn, PyTorch, or TensorFlow, as all development will be conducted in Python. Essential skills include experience with data analysis and visualization libraries (numpy, pandas, matplotlib), understanding of ML concepts including training/validation/testing, overfitting, regularization, and cross-validation, and comfort with Git/GitHub for version control. Strong problem-solving abilities, independent work skills, and excellent written and verbal communication are required, along with genuine interest in astronomy and willingness to learn about stellar physics.

The ideal candidate is a sophomore or junior undergraduate student (preferred to enable multi-semester engagement) who is interested in continuing work over multiple semesters and pursuing first-author publication. No prior astronomy research experience is required.

When a star engulfs a planet, it can pick up two chemical fingerprints: an excess of refractory (rock-forming) elements, which shows up as a rising trend of abundance with condensation temperature, and extra lithium, which ordinary main-sequence stars slowly destroy. We do not have a list of which stars are engulfment cases, so this project frames discovery as anomaly detection: first find the chemically unusual stars, then test whether their peculiarity really points to planetary engulfment.

The intern will train a neural density model (a normalizing flow or autoencoder) on the roughly thirty elemental abundances plus lithium in the GALAH DR4 dataset, which contains nearly one million stars. By conditioning on temperature, gravity, and metallicity, the model learns what "chemically normal" looks like for each kind of star, flagging the stars it fits poorly as outliers. Those outliers are ranked along the two physically meaningful axes, the condensation-temperature slope and the lithium excess, to pull engulfment-like candidates out from other kinds of oddballs such as binaries or evolved-star pollution.

The scientific crux is separating real engulfment from look-alikes, because lithium also tracks age and a refractory excess can simply reflect a star's birth gas. To handle this, each candidate will be compared against control stars that match it in temperature, gravity, metallicity, and age, ideally stars born together with it in a wide binary or cluster. If the candidate stands out from these near-twins specifically in lithium and refractory slope, engulfment becomes the most likely explanation.

The intern will release two products: the anomaly-detection framework as an open-source Python package, and a public, ranked catalog of engulfment candidates. A central goal is for the intern to lead a first-author publication on the work. The project suits a computer science student interested in unsupervised and generative deep learning, and no astronomy background is required, since the needed concepts will be taught along the way.

Interns are expected to be able to sit for extended periods. Specific physical demands will be discussed with mentors during the interview process.

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