Summer workers, Machine Learning Integrators for Control Systems

Big Science Sweden

Lunds kommun

On-site

SEK 245,520 - 334,800

Full time

14 days+

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Job summary

En forskningsinstitution i Lund söker två sommarstudenter för att hjälpa till med att integrera maskininlärning i kontrollsystem. Du kommer att ansluta tränade ML-modeller till EPICS-ramverket så att operatörerna kan se live-prediktioner. Du bör vara universitetsstudent inom datavetenskap eller liknande och ha erfarenhet av Python och PyTorch. Tjänsten är på plats under 8–12 veckor sommaren 2026.

Qualifications

  • Universitetsstudent inom datavetenskap, teknik, fysik eller relaterat område.
  • Erfarenhet av programmering i Python.
  • Praktisk erfarenhet av PyTorch.

Responsibilities

  • Anslut tränade ML-modeller till EPICS-ramverket.
  • Träna, validera och deployera ytterligare ML-modeller.

Skills

Python
PyTorch
Linux

Education

Universitetsstudier inom datavetenskap, teknik eller fysik

Job description

Summer workers, Machine Learning Integrators for Control Systems

Datum: 3 maj, 2026 Tid: 11:59

Placering: ESS

Mer information
About the position

The Integrated Control System division (ICS), part of the Operations and Machine Directorate, is responsible for the EPICS-based control system of the facility. We are looking for two summer students to help bring machine learning into the control system. You will connect trained ML models to the EPICS framework so that operators can see live predictions and diagnostics on their display screens. As time and capacity allow, you will also train, validate, and deploy additional ML models for related subsystems, following the same integration workflow.

About you

You are a university student in computer science, engineering, physics, or a related field. You are comfortable programming in Python and have hands-on experience with PyTorch. Linux skills are valuable and any prior exposure to EPICS control systems is a bonus.

Duration and location

You will work on-site at ESS in Lund during the summer 2026, for approx. 8–12 weeks. Planned start date per agreement.

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