In this role, you will join a multidisciplinary team of laser physicists, control engineers, and machine learning specialists to develop the automation layer of TALISMAN, a project aimed at automating laser ionization for the production of medically relevant radionuclides. The work combines hands‑on laboratory activities with the design, prototyping, and implementation of control algorithms and software. At the intersection of applied physics, control systems, and computing, you will work directly with experimental hardware while applying modern algorithmic methods, contributing to a concrete deliverable and developing technical and collaborative skills in a unique scientific environment.
Your responsibilities
Automate critical steps of the ion beam production chain, such as laser preparation, beam‑path alignment, ion beam optimisation, and continuous monitoring, building on existing control interfaces.
Engage with project stakeholders to define and refine requirements throughout the project lifecycle.
Work hands‑on in the laboratory with the laser setup alongside laser physicists and controls experts.
Research and prototype suitable control algorithms, drawing from classical control, optimal control, and machine learning where necessary.
Engineer the chosen approaches into automation and optimisation modules that integrate with existing device control software, following sound software engineering practices.
Drive solutions toward operational deployment, including validation and documentation.
Your profile
Initial experience working in an experimental laboratory/test‑bench environment (optics, laser systems, or similar).
Practical experience with control and/or optimisation algorithms, or machine learning (ML) methods.
Programming experience in Python, including scientific and ML libraries.
Practical application of software engineering practices, including version control (Git).
Advantageous
Experience applying control algorithms and ML techniques to real‑world systems and bringing solutions to operational deployment.
Experience interfacing software with laboratory hardware (motors, cameras, sensors, or similar).
Experience with beam transport and alignment.
Prior exposure to LabVIEW.
Skills
Solid grounding in physics or engineering.
Strong problem‑solving skills and the ability to work independently.
Ability to translate algorithmic concepts into working, maintainable code.
Familiarity with image processing and analysis techniques.
Excellent communication and collaboration skills, comfortable working across physics, controls, and software domains.
Spoken and written English, with a commitment to learn French.
Eligibility criteria
You are a national of a CERN Member or Associate Member State.
By the application deadline, you have a maximum of two years of professional experience since graduation in Applied Physics, Computer Science (or a related field) and your highest educational qualification is either a Bachelor's or Master's degree.
You have never had a CERN fellow or graduate contract before.
Applicants without a university degree are not eligible.
Applicants with a PhD are not eligible.
Additional Information
Job closing date: 14.06.2026 at 23:59 CEST.
Contract duration: 24 months, with a possible extension up to 36 months maximum.
Working hours: 40 hours per week.
Job flexibility: Fully Onsite.
Target start date: 01-August-2026.
Job reference: BE-OP-PS-2026-139-GRAE.
Field of work: Applied Physics.
Benchmark job: 200140 – Applied Physicist.
Global Benefits
A monthly stipend between 5266‑5793 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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