This position develops AI solutions and proofs of concept in the field of industrial production and engineering systems. As part of an innovative team you perform applied research with a focus on Deep Learning technologies (e.g. image classiciation, object recognition) and Reinforcement Learning (e.g. optimization problems) and related industrial problem setups. Thereby, close collaboration with the business units of Siemens Digital Industries and the AI innovation department in headquaters in Germany is required.
Ready to apply your knowledge in AI to real-world problems and work in a team of highly motivated AI experts? Then this is the right position for you!
• Working with business unit (e.g. product managers, business development, system architects), Siemens factories and selected customers to identify AI use cases and understand requirements.
• Implementation of proofs of concept, and investigation and implementation of different solution alternatives.
• Investigation of the latest AI technologies and assessment of their benefits.
• Application of AI technologies to real-world problems in industrial use cases (e.g. automation, control, etc.)
Required Knowledge / Skills, Education, and Experience
• Strong independent AI technical survey ability and implementation skills. Able to review and implement state of art.
• Good at source code reading and incremental development ability
• Strong programming skills (Python is a must)
• Good mathematics understanding (linear algebra, probability and statistic, convex optimization)
• Knowledge in both classic machine learning (decision-tree, svm, etc.) and deep learning (classification, object detection, embedding learning, etc).
• Familiar with scikit-learn, pytorch, tensorflow or other related frameworks/libraries.
• Excellent interpersonal and communication skills in English (verbal & written).
• Willingness to learn every day and curiosity to learn how new technologies can make an impact in industry business.
Knowledge as a Plus
• Knowledge of classic computer vision techniques (Canny, SIFT, etc.)
• Knowledge of reinforcement learning (model-based and model free RL, on- and off-policy learning, inverse-RL, etc.)
• Knowledge of data efficient learning (metric-learning, meta-learning, etc.)