Junior Scientist, Embedded AI & ML Research

Karlstad University; Österreich; Vor Ort; EUR 41.000 - 49.000; Vollzeit

Benefits dieser Stelle

Relocation support

Training opportunities

Family-friendly policy

Meal allowance

Klimaticket subsidy

Open door policy

Zusammenfassung

SAL is seeking a researcher to join its Embedded Systems group in Austria, focusing on trustworthy AI for industrial and scientific partnerships. You will advance anomaly detection, predictive maintenance, and learning methods across domains, prioritising safety and reliability while collaborating with peers in cross-disciplinary teams.

You will contribute to federated learning, reinforcement learning, and automata learning, applying AI techniques to manufacturing, electronics, and energy

Qualifikationen

• Master's degree in Computer Science, Robotics, Electrical Engineering, Applied Mathematics, Data Science, Physics, or a related field.
• Strong programming skills in Python.
• Experience with code management tools such as Git (GitLab).
• Experience with frameworks such as PyTorch and TensorFlow.
• Experience applying statistical techniques to analyse data and detect outliers.

Aufgaben

• Conduct innovative research in your field of expertise and address challenging scientific questions.
• Analyse research questions and design methods with senior experts.
• Design and carry out experiments, simulations and research activities; evaluate results.
• Translate research findings into practical AI technologies and applications.
• Present results within the team and at conferences; contribute to publications and reports.
• Follow developments and expand expertise through training and professional development.

Kenntnisse

Python

Machine learning

English

Communication

Ausbildung

Master's degree (CS/Robotics/EE/Applied Math/Data Science/Physics)

Tools

Git (GitLab)

PyTorch

TensorFlow

Data analysis

Jobbeschreibung

SAL is seeking a researcher to join its Embedded Systems group in Austria, focusing on trustworthy AI for industrial and scientific partnerships. You will advance anomaly detection, predictive maintenance, and learning methods across domains, prioritising safety and reliability while collaborating with peers in cross-disciplinary teams.

You will contribute to federated learning, reinforcement learning, and automata learning, applying AI techniques to manufacturing, electronics, and energy