At CERN, the European Organisation for Nuclear Research, physicists and engineers are probing the fundamental structure of the universe. Using the world's largest and most complex scientific instruments, they study the basic constituents of matter - fundamental particles that are made to collide together at close to the speed of light. The process gives physicists clues about how particles interact, and provides insights into the fundamental laws of nature.
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Job description
Foundation models represent one of the most powerful and promising advancements in Deep Learning, and their application to scientific domains is a rapidly evolving area of research.
At CERN's IT-CE group, we are exploring the development of a foundation model tailored to particle physics: one capable of interpreting the behaviour of particles within High Energy Physics (HEP) detectors and supporting a wide range of tasks relevant to experimental data processing.
This work is carried out in close collaboration with a multidisciplinary team of experts from leading institutes across Europe in the context of TURING, a EC funded project.
As a successful candidate, you will contribute to the design and training of transformer-based architectures using data from calorimeters, specialised detectors that measure particle energy in collider experiments.
Your work will involve investigating methodologies such as self-supervised learning to enable multi-task capabilities, while optimising for computational efficiency.
We are looking for candidates with a strong background in Computer Science or a closely related field. Experience with Deep Learning, transformer models, or scientific data is highly desirable.
Your responsibilities
Take an active role within the TURING project, leading the performance evaluation and use case validation tasks.
Developing a robust prototype capable of generalising across multiple detector use cases.
Contributing to tightening the collaborations with the CERN experimental physics community on the topic of foundation models for HEP.
Your profile
Proven experience developing, training, and deploying deep learning models in production environments, with hands-on expertise in neural network architectures, large-scale data processing, model optimisation, and performance evaluation.
Skills
Strong proficiency in Python and deep learning frameworks (PyTorch and/or TensorFlow), machine learning algorithms, data analysis libraries (NumPy, Pandas), GPU-based training, software engineering best practices, Git version control, Docker, and MLOps tools for experiment tracking and model deployment.
Spoken and written English, with a commitment to learn French.
Eligibility criteria:
You are a national of a CERN Member or Associate Member State.
You have a professional background in Data Science, Mathematics or Physics (or a related field) and have either:
a Master's degree with 2 to 6 years of post-graduation professional experience;
or a PhD with no more than 3 years of post-graduation professional experience.
You have never had a CERN fellow or graduate contract before.
What we offer
A monthly stipend between 6,372-7,004 Swiss Francs per month (tax free) depending on your degree.
30 days of paid leave per year plus 2 weeks' annual closure.
Coverage by CERN’s comprehensive health insurance scheme (for yourself, your spouse and children), and membership of the CERN Pension Fund.
Family, child and infant monthly allowances depending on your individual circumstances.
A relocation package (installation grant and travel expenses) depending on your individual circumstances.
Possibility to extend your contract up to 36 months.
On-the-job and formal training including language classes.
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