Astronomy ML Intern: AnomalyMatch in Spectra & Light Curves

European Space Agency - ESA; Villanueva de la Cañada; On-site; EUR 7,800 - 11,000; Part time

Job summary

European Space Agency (ESA) invites applications for an internship in the Science Operations Department. The project focuses on discovering astronomical anomalies in light curves and spectra using AnomalyMatch, extending it to new data types and validating on real datasets.

The intern will adapt machine-learning methods, work with colleagues across divisions, and explore cross-telescope data integration. Fluency in English or French is expected, and candidates should be enrolled for the full

Qualifications

• Must be a university student, preferably in your final or second-to-last year of university course at Master's level.
• Must be enrolled at your university for the entire duration of the internship.
• Strong Python programming skills.
• Background in machine learning and neural networks.
• Experience with PyTorch or a similar deep-learning framework.
• Familiarity with astronomical spectra, light curves, or scientific data analysis would be advantageous.

Responsibilities

• Adapt AnomalyMatch for spectra and/or light curves and validate on real astronomical datasets.
• Extend the framework to combine measurements from different telescopes or instruments.
• Collaborate with ESA teams and contribute to data analysis pipelines.

Skills

Python programming

Machine learning

Neural networks

PyTorch

Education

Master's degree level (student)

Tools

PyTorch

Job description

European Space Agency (ESA) invites applications for an internship in the Science Operations Department. The project focuses on discovering astronomical anomalies in light curves and spectra using AnomalyMatch, extending it to new data types and validating on real datasets.

The intern will adapt machine-learning methods, work with colleagues across divisions, and explore cross-telescope data integration. Fluency in English or French is expected, and candidates should be enrolled for the full