Attention! Please, submit your applications here: https://career.silicon-austria-labs.com/Junior-Scientist-AI-all-genders-eng-j116.html
As part of SAL’s Embedded Systems research team, you will be responsible for developing and testing resilient and trustworthy AI in applied research projects with industrial and scientific partners, as well as colleagues from other SAL units.
You will support research and development in areas such as advanced anomaly detection, out-of-control action planning, predictive maintenance algorithms and automata learning.
You will contribute to the development of scalable, distributed AI and learning methods, such as federated learning, federated distillation and reinforcement learning, and apply these techniques across domains including manufacturing, electronics and energy, while prioritising safety, reliability and trustworthy AI practices.
Your future tasks include:
- Conduct innovative research in your field of expertise and investigate new approaches to address challenging scientific and technological questions.
- Analyse research questions and application requirements, identify suitable methods and develop solutions in collaboration with experienced colleagues and senior experts.
- Design, plan and carry out experiments, simulations and research activities, and critically evaluate and interpret the results.
- Translate research findings into practical solutions and contribute to the development of innovative technologies and applications.
- Present and discuss your results within the research team, at scientific conferences and with external partners.
- Contribute to scientific publications, technical reports, reviews, project proposals and research documentation.
- Follow current scientific and technological developments and continuously expand your expertise through research, training and professional development.
- Build a strong scientific profile and develop towards becoming a recognised expert in your field.
- Actively contribute to knowledge exchange and foster collaboration across interdisciplinary research teams.
Your profile:
- Master’s degree in Computer Science, Robotics, Electrical Engineering, Applied Mathematics, Data Science, Physics, or a related field.
- Good programming skills in Python.
- Experience with code management tools such as Git (GitLab).
- Experience with frameworks such as PyTorch and TensorFlow.
- Experience of applying statistical techniques to analyse data, detect outliers and select and sample datasets.
- Good knowledge and a proven track record in machine learning, including classical methods such as SVMs and decision trees, as well as deep learning methods such as LSTMs, CNNs and transformers.
- Specific experience in Large Language Models (LLMs) and Generative AI, Embedded AI and TinyML, Reinforcement Learning, Formal Methods and Automata Learning.
- Experience with edge platforms (Raspberry Pi, NVIDIA Jetson).
- Fluency in English is essential; knowledge of German would be advantageous.
- Good communication skills, including basic presentation and academic writing skills.