Research Assistant in Machine Learning for Complex Industrial Systems
The Role
As a Research Assistant in Machine Learning for Complex Industrial Systems at Flexsis Bulle, you will be responsible for developing innovative approaches using advanced statistical, machine learning, and deep learning methods for intelligent maintenance systems. The role involves developing data-driven algorithms for detection, diagnostics, and prognostics of failures in industrial assets, collaborating with industry partners, and publishing research outcomes.
Requirements
Candidates should have a master's degree in engineering, physics, computer science, statistics, or related fields, with programming experience in Python. Knowledge of TensorFlow, Keras/PyTorch, and data science methods is desired. Strong English communication skills are required, and German proficiency is a plus. Applicants should be eager to learn and apply new methods in real industrial systems.