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Postdoc Mathematical Optimization for Human Centric Lighting

Eindhoven University of Technology has an opening postdoctoral scholar position in signal processing. Applications will be evaluated on a continuous basis.

Applications are invited for aPostdoc position at the Faculty of Electrical Engineering at Eindhoven University of Technology, the Netherlands, in collaboration with the Intelligent Lighting Institute.

The Intelligent Lighting Institute (ILI) is TU/e's interdepartmental institute for innovations in lighting. It was established in 2010 and is home to multidisciplinary research around novel intelligent lighting solutions, with a special emphasis on how these new solutions might affect people. In addition, ILI aims at providing scientific evidence for the claims that go with these novel lighting solutions.

The goal of this project is to make lighting control systems more centered towards the human user. This requires not only better insights in how humans experience light but also demands quantified models and optimization algorithms that are executed by automated lighting control systems.

Despite the growing scientific understanding of the impact of light on, for instance, wellbeing, performance, circadian rhythms and sleep, benefits of this understanding cannot (yet) easily be harvested in practical systems. We lack scalable algorithms that can be used in automated systems and that can be deployed in different environments without extensive tuning by experienced lighting experts. Scalability towards broad deployment is a key sub goal of this project. Although control theory and optimizations using statistical signal processing are well established areas, to date they are not widely used for lighting control.

We are confident that better models of human experience and perception can improve automatic control systems. However, it is not straightforward to capture human experience in equations. An important step is to quantify the reliability of such expressions and to take this into account in probabilistic algorithms.

The research project is conducted under the leadership of Prof. J.P. Linnartz of the SPS group in the faculty of Electrical Engineering. There is an active collaboration with groups from the departments of Electrical Engineering, Computer Science, Innovation Sciences and the Built Environment. Moreover, the SPS group has a strong research relation with the Kempenhaeghe Sleep Expertise Center.

This research is expected to contribute to the activities within TU/e's Intelligent Lighting Institute (ILI) is cooperation with Signify (Philips Lighting) Research and funding agencies. The position is closely related to the running research project "OptiLight". It aims at algorithms based on probabilistic mathematical models for human experience and the human chronological rhythm. Hence, we are looking for candidates with a strength both in human aspects and in mathematical techniques. The candidate is expected to work, share and use results in a multi-disciplinary setting and, yet still excel, e.g. publish in high-impact journals, in specific focus areas.

The overall goal of this project is to make lighting control systems more centered towards the human user. This requires not only better insights in how humans experience light but also demands quantified models and optimization algorithms that are executed by automated lighting control systems. Despite the growing scientific understanding of the impact of light on, for instance, wellbeing, performance, circadian rhythms and sleep, benefits of this understanding cannot (yet) easily be harvested in practical systems. We lack scalable algorithms that can be used in automated systems and that can be deployed in different environments without extensive tuning by experienced lighting experts. Scalability towards broad deployment is a key sub goal of this project.

Humans want to experience light as a natural given. Having to adjust the light setting regularly is not at-tractive. Moreover, people are usually not aware of the longer-term effects of light so they don't not necessarily select the optimal light setting. On the other hand, automatic controls often fail to offer a comfortable and unobtrusive natural experience and even tend to irritate people. Hence, there exists a huge gap be-tween results obtained in controlled environments and practical deployment.

Although control theory and optimizations using statistical signal processing are well established areas, to date they not widely used for lighting control. Yet, in audio and video processing and in gaming, models on human factors are successfully being used by automated systems. Hence we have reasons to be confident that better models human experience and perception can improve automatic control systems. However, it is not straightforward to capture human experience in equations. We believe that an important step is to better quantify the reliability of such expressions and to take this into account in probabilistic algorithms.

As a first phase in this specific subproject, we plan to extend of base of models by translating insights and results from user tests of human experience into quantified models, extending work of earlier research projects. Then we use these models and reliability information from sensors in formal optimizations. We plan to apply the principles of Statistical Signal Processing to Lighting Control and to account for incomplete knowledge that the system has about the users and learn from (hopefully) very sparse user interventions in the lighting conditions.

The ideal candidate is pro-active, highly motivated, and independent, and has proven experience with the subject. The candidate has also affinity with giving lectures and has good written and oral communication skills in English.

More information on employment conditions can be found here: http://www.tue.nl/en/university/working-at-tue/working-conditions/ .

Candidates will be selected based on graduation mark and proficiency at university including consideration of the reputation of the university, relevant experience and skills, writing skills and publications, work experience as well as performance in relevant modeling exercises and interviews.


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