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Research associate/PhD candidate (f/m/d) on the topic »Machine Learning on Distributed Resource-Constrained Systems«

Organizational unit

Institute for Computer Engineering (ITEC)

Job description

Since many years, the Chair for Embedded Systems works internationally successfully in the areas of computer engineering, such as distributed embedded systems. Many interesting and open problems in these areas need to be addressed to successfully deploy such systems in modern application domains. As an example, the most urgent questions about cooperative distributed machine learning are highlighted in the following.

The number of devices that interact with the real world (semi-)autonomously increases strongly, not least because of the rise of the IoT. Machine learning techniques are more and more commonly used to guide these interactions. Thereby, every device collects experiences at run-time that shall be used to continuously train the employed machine learning models. Learning is performed distributedly on every device where the experiences are collected. However, realizing devices in large quantities requires low cost, low electrical power, and low energy, leading to resource-constrained devices that cannot perform exhaustive training of complex models. The research question addressed in this project is: How can a system with resource-constrained devices learn in a cooperative and distributed manner? One aspect of this question is how to cope with the resource constraints. Should training still be performed locally by reducing the computational complexity of the training, or should parts of the training process be offloaded to other devices? Developed techniques must be scalable because the number of devices may be large, and may be prone to reliability requirements where central coordination as a single point of failure is not allowed. Additionally, privacy concerns may require that the learning is performed in a way that no device can reverse-engineer information about experiences made on another device, even though all devices cooperatively learn a joint model. Such open research questions for cooperative distributed machine learning shall be investigated and addressed in this research project.

Starting date

as soon as possible

Personal qualification

You must have a very good Master's degree (or equivalent) in CS or EE with background or specialization in the above-mentioned topics. The ideal candidate (f/m/d) shows a strong interest and motivation to deepen in these topics to a level required for a doctorate. Programming skills in C/C++, and scripting languages will be required, and fluency in written and spoken English is a prerequisite. We are looking for a highly motivated candidate (f/m/d) with a strong commitment to research ethics and teamwork. Good communicative skills are mandatory due to the interdisciplinary structure of the project and the team.

Salary

Salary category 13, depending on the fulfillment of professional and personal requirements.

Contract duration

limited to four years with the possibility to obtain a Ph.D.

Application up to

15.10.2020

Contact person in line-management

For technical information, please contact Prof. Henkel (henkel@kit.edu topic: Application CES_SWC_ML).

Please apply online using the button below for this vacancy number 1019/2020.

Ausschreibungsnummer: 1019/2020

We prefer to balance the number of employees (f/m/d). Therefore we kindly ask female applicants to apply for this job.

If qualified, severely disabled persons will be preferred.

Contact

Personnel Support is provided by:
Ms Brückner
Phone: +49 721 608-42016,

Kaiserstr. 12, 76131 Karlsruhe