Je suis intéressé
In recent years the popularity of machine learning methods has increased dramatically. CSEM has a long experience in this field with technologies that have now reached the market. Huge advancements have taken place in image analysis and natural language processing. In particular the usage of Recurrent Neural Networks has reported ground breaking results in natural language processing. The application of RNN methods has so far been mainly concentrated in audio signals, and the field of multi-dimensional data and temporally correlated image sequences are exciting new areas of research. Modelling temporal processes can offer new insights in to problems in Industrial Production, Remote Sensing and Earth Observation. This thesis project will develop methodologies for modelling multi-dimensional time-based data sets using neural networks.
The work of the PhD student will revolve around the design of appropriate model architectures and efficient training methods. The candidate will have the opportunity to explore a variety of data sets with direct industrial applications while working in close contact with the Institute of Machine Learning at ETH Zurich and R&D divisions at CSEM to drive the research toward practical industrial needs.
Your responsibility will also include to:
• Disseminate and report your results using international platforms
• Take active part in the activities of the Robotics and Automation group and the Institute of Machine Learning at ETH Zurich.
• Master degree in Computer Science, Mathematics, Physics, or related fields
• Solid programming skills
• Good written and spoken English
• Strong team-working abilities
• Enthusiastic, proactive and autonomous
• Excellent communication skills
• Former experience with neural networks in particular with RNN is a plus
• Experience in computer vision and signal processing is a plus.
CSEM offers a stimulating and multidisciplinary work environment with the opportunity to benefit from an intensive supervision for your thesis and to develop your analog/mixed signal design skills. Moreover, depending on the results, there might be an option to become co-author of a conference/journal paper. Finally, you will have the opportunity to benefit from excellent social security conditions and to evolve within a multicultural company which clearly promotes an employee-driven culture.
We look forward to receiving your complete application file at email@example.com, mentioning ref. A611.2017-58 in the subject. Preference will be given to professionals applying directly.