- Applicants should have, or expect to receive, a Master of Science degree or equivalent in a relevant electrical engineering or applied physics discipline.
- Besides good subject knowledge, emphasis will be on creative thinking, motivation, ability to cooperate, initiative to work independently and personal suitability for research training.
- You will need to combine expertise of statistical signal processing, data science, electromagnetic theory and measurement techniques, and a solid background in some of these area is required.
- Proficiency in using scientific and engineering software packages such as Python, MatLab, ADS, CST etc. are advantageous.
- A research-oriented attitude.
- Ability to work in a team and interested in collaborating with industrial partners of LoLiPoP IoT.
- Motivated to develop your teaching skills and coach students.
- Fluent in spoken and written English (C1 level).
- Full-time employment for four years, with an intermediate evaluation (go/no-go) after nine months. You will spend 10% of your employment on teaching tasks.
- Salary and benefits (such as a pension scheme, paid pregnancy and maternity leave, partially paid parental leave) in accordance with the Collective Labour Agreement for Dutch Universities, scale P (min. €2,770 max. €3,539).
- A year-end bonus of 8.3% and annual vacation pay of 8%.
- High-quality training programs and other support to grow into a self-aware, autonomous scientific researcher. At TU/e we challenge you to take charge of your own learning process.
- An excellent technical infrastructure, on-campus children's day care and sports facilities.
- An allowance for commuting, working from home and internet costs.
- A Staff Immigration Team and a tax compensation scheme (the 30% facility) for international candidates
PhD Using Wireless Networks as an environment Sensor in Complex Environments - Eindhoven, Nederland - Eindhoven University of Technology
Beschrijving
Job Description
In this project you will focus on algorithms to extract meaningful information by analysing the properties of radio channels and of the EMC environment. You will start with making an overview of the various approaches for RF sensing that have been presented thus far. Many solutions involve machine learning to learn the environment. However, by looking deeper into known properties of radio propagation in multipath environments, our first insights promise substantial improvement opportunities. You will find effective methods to process noisy, not always fully reliable sensor data, that may not always be available, including RF sensing, in order to estimate the locations and movements of individuals inside an office environment.
The main results will be on the creation and refinement of algorithms, but you will measure radio environments and try algorithms based on measured date. Preferable you will demonstrate the algorithm in a real-time demonstration set-up.
Besides research you will also contribute to education within the department. Apart from supervising BSc and MSc students in their research projects, other assistance in education, e.g. in bachelor courses, is usually limited to around 5% of your contract time.
You will work in a team of other PhD candidates, who will also use sensing data in algorithms, for instance to estimate the activities and to use sensor data to enhance comfort, circadian rhythms, ability to perform tasks, etc.
Job requirements
Conditions of employment
A meaningful job in a dynamic and ambitious university, in an interdisciplinary setting and within an international network. You will work on a beautiful, green campus within walking distance of the central train station. In addition, we offer you: