Vera C. Rubin Observatory
Rubin's survey is designed around a nightly flow of roughly ten terabytes, with millions of real-time alerts and billions of catalogued objects across its decade-long survey.
Career Lab · Current focus
Is a career in astronomy and astrophysics for you?
Modern astronomy is becoming one of the most data-intensive areas of science. New ground- and space-based observatories are collecting the Universe at unprecedented scale, and turning those observations into discoveries increasingly depends on people who can combine physics with programming, statistics, scientific data analysis and artificial intelligence.
Astronomy in the data era
The telescope is only the beginning.
Astronomy is moving into a new era. Wide-field sky surveys, giant radio arrays, gravitational-wave detectors and next-generation space telescopes are producing images, spectra, catalogues and time-series measurements on a scale that cannot be explored manually.
The challenge is no longer only to collect signals from the Universe. Scientists must search, clean, combine, model and interpret immense datasets. Modern observatories therefore depend on scientific software, high-performance computing, statistics, machine learning and increasingly AI-assisted analysis. The same skills also transfer well beyond astronomy into scientific computing, space science, data science and other technology-led careers.
SKA Observatory
The Square Kilometre Array is expected to archive more than 700 petabytes of science data each year, supported by high-performance computing and a global network of regional data centres.
Roman Space Telescope
NASA's Nancy Grace Roman Space Telescope is expected to generate about 20 petabytes of processed data over its five-year primary mission.
LIGO–Virgo–KAGRA
Gravitational-wave astronomy is now a data-analysis discipline in its own right. The LVK network combines multiple ground-based detectors, low-latency searches, large-scale computing and open scientific data to identify and characterise signals from compact-object mergers.
Data-scale examples: Rubin Observatory, SKAO, NASA Roman, and LIGO–Virgo–KAGRA.
Where the opportunity expands
Astrophysics now sits at the intersection of science and data.
Today's astronomer may work with a telescope, but just as often with Python, algorithms, cloud platforms, simulations and large scientific datasets.
Time-domain astronomy
Variable stars, supernovae, exoplanet transits, gamma-ray bursts and other changing or short-lived events.
Multi-messenger astronomy
Gravitational waves, electromagnetic observations, neutrinos and other messengers combined to study the same cosmic event.
Large astronomical surveys
Catalogues containing millions or billions of sources used to investigate stars, galaxies, dark matter, dark energy and cosmic evolution.
AI-assisted discovery
Classification, anomaly detection, computer vision and machine learning used to find patterns and prioritise discoveries within datasets too large to inspect manually.
Six-month career discovery pathway
Don't choose the career from a prospectus. Experience it.
Our Career Lab pathway is designed as a stepping stone between curiosity and university study. Learners spend approximately six months exploring astrophysics through scientific questions, data, computational methods and university-style academic work.
Stars and stellar evolution
Use observational data to understand stellar properties, colour, luminosity, temperature and how stars change over time.
Binary stars and light curves
Explore time-series data, periodic behaviour and eclipsing systems, learning how astronomers infer physical information from changing brightness.
Exoplanets
Investigate transit data and learn how repeated changes in stellar brightness can reveal planets around other stars.
Galaxies and cosmic evolution
Work with survey catalogues, galaxy properties and redshift to explore how the Universe changes across cosmic time.
Gamma-ray bursts and transients
Study energetic, short-lived events through light curves, duration measures and statistical population analysis.
Gravitational-wave astronomy
Meet real detector concepts: strain data, noise, time-frequency analysis and how signals from compact-object mergers can be identified.
Python, data analysis and AI
Build practical skills in scientific Python, visualisation, statistics, classification and introductory machine learning through astrophysical problems.
Independent data project
Finish by asking a scientific question, analysing a dataset and communicating the result through a structured report and presentation.
University readiness
Learn how to study science, not only what to study.
Academic transition is compulsory throughout the programme. Learners practise the habits and conventions expected in higher education before they arrive at university.
Scientific writingCritical thinkingReferencingLiterature searchingResearch methodologyAcademic integrityLearning outcomes & rubricsStatistics & uncertaintyData visualisationPresenting scientific resultsIndependent studyProject planning
Make an informed choice
Six months later, the question should be easier to answer.
You may discover that astrophysics is exactly where you want to go. You may find that you prefer data science, AI, computing or another area of physics. Either outcome is valuable. Career Lab is designed to help you make that discovery before committing years of study and a substantial financial investment to a university degree.
Current Career Lab