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Master Thesis Machine learning techniques for chemical sensors

  • Ort: München
  • Beginn: 01.04.2019
Online bewerben Referenznummer: 35020_1548696610

Referenznummer: 35020_1548696610

Das erwartet Sie bei uns

Part of your life. Part of tomorrow.

We make life easier, safer and greener - with technology that achieves more, consumes less and is accessible to everyone. Microelectronics from Infineon is the key to a better future. Efficient use of energy, environmentally-friendly mobility and security in a connected world - we solve some of the most critical challenges that our society faces while taking a conscientious approach to the use of natural resources.

  • Power Management & Multimarket (PMM) drives leading-edge power management, sensing and data transfer capabilities -

Infineon PMM semiconductors play a vital role in enabling intelligent power management, smart sensitivity as well as fast and reliable data processing in an increasingly digitalized world.

Our leading-edge power devices make chargers, adapters, power tools and lighting systems smarter, smaller, lighter and more energy-efficient. Our trusted sensors increase the context sensitivity of "things" and systems such as HMI, and our RF chips power fast and reliable data communication.

For this thesis, you will develop a suite of algorithms in a system level simulator and find map clustering and data aggregation solutions on a possible architecture. Therefore, your responsibilities will include:

* Development of machine learning approaches for the classifications and analysis of data from chemical sensors;
* Study the tradeoff between computational requirements and learning model's accuracy;
* Assessment of the algorithms performance on a real measurement data base.

You are best suited for this position if you:

* Are studying Electrical Engineering or Computer Science - background in Physics or Chemistry would be a plus;
* Have knowledge in signal processing for sensor applications;
* Have knowledge in machine learning basics, including clustering and compression algorithms;
* Have programming knowledge in Python, Matlab and C/C++;
* Are fluent in English - German would be a plus.

Please attach the following documents to your application:

* Your CV in English;
* Copy of your certificate of matriculation at university;
* Copy of your latest grades transcript;
* Copy of your high school certificate.

Take it from our students
"Working as a student for PMM SYS is a very good experience. I got the opportunity to meet and discuss with new people. The atmosphere in the team is friendly, everyone is willing to help when you ask them. Also, I gained a lot of knowledge by working on machine learning applications through different projects." - Emna

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  • Ort: München
  • Beginn: 01.04.2019

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