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Internship in Domain Adaptation for Time-Series Using Deep-Learning (80-100%)


ABB
BadenLocation
Baden
19 hours ago
Posted date
19 hours ago
N/A
Minimum level
N/A
Full-timeEmployment type
Full-time
OtherJob category
Other
At ABB, we help industries run leaner and cleaner-and every person here makes that happen. You'll be empowered to lead, supported to grow, and proud of the impact we create together. Join us and help run what runs the world.

This position reports to:
Research Department Manager

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Your role and responsibilities
In this role, you will have the opportunity to gain vocational experience through a temporary work placement. Each day, you will acquire knowledge by performing tasks as directed. You will also showcase your expertise by supporting ABB's operations and enhancing personal education/employment opportunities.

The work model for the role is: #LI-Hybrid

This role is contributing to the sensing and analytics research group in Baden-Dättwil, Aargau, Switzerland.

Main stakeholders are Automation and Electrification divisions.

Ignite your career in data science and signal processing by contributing to our innovative projects as a research intern. Leverage your technical skills to enhance the precision and efficiency of our measurement systems.

You will be mainly accountable for:
  • Exploring new ideas and developing early‑stage AI approaches for time‑series data augmentation, while working closely with different teams to support ongoing activities.
  • Reviewing and comparing generated data with real field data to help reduce differences and improve the usefulness of the results.
  • Measuring experimental data in the laboratory to enrich your dataset.
  • Documenting your work clearly, including methods, findings, and lessons learned, to ensure the team can easily follow and build on your progress.
  • Using simple physics‑based simulations (e.g., Modelica or LTSpice) when helpful to complement the AI‑generated data. (Optional)


Our team dynamics

You will join a dynamic, talented and diverse team, where you will be able to thrive.

Qualifications for the role
  • You are currently pursuing a degree (enrollment to 5th semester or higher is essential) in data science, signal processing, electronics or a related discipline
  • You are highly skilled in physics and electronics and you enjoy working with python, machine learning
  • You are the innovator who makes things work, and the scientist who investigates why they don't
  • You are comfortable communicating in English
  • You hold a current valid VISA/work permit for Switzerland or a confirmation for a mandatory internship from your university


More about us

Electrification provides leading electrical distribution and management technologies, solutions and services to electrify the world in a safe, smart and sustainable way. The portfolio includes medium- and low-voltage electrical components, switchgear, digital devices, enclosures, and circuit breakers, among others. With our products, solutions and services, we collaborate with customers to improve power delivery and security, enhance energy management, efficiency and operational reliability, as we seek to achieve a low carbon society. go.abb/electrification

We look forward to receiving your application.

The recruiting process is being led by Mara Werne, Talent Partner at ABB Switzerland, Ltd.

If you want to discover more about ABB, take another look at our website www.abb.com.

Call to action

Be part of something bigger. This is where progress is powered, teams initiate action, and we move the world forward together. Run What Runs the World.

Building a cleaner, smarter future takes all kinds of minds: the curious, the courageous, and the creative. That's why we welcome people from all backgrounds and experiences.

Ready to make an impact?

Apply today or visit https://www.abb.com to learn more about the impact of our solutions across the globe.
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JOB SUMMARY
Internship in Domain Adaptation for Time-Series Using Deep-Learning (80-100%)
ABB
Baden
19 hours ago
N/A
Full-time

Internship in Domain Adaptation for Time-Series Using Deep-Learning (80-100%)