4 PhD candidate positions in Activity Recognition and Ubiquitous Computing

about 3 years ago | The position may have been removed or expired!


4 PhD candidate positions in Activity Recognition and Ubiquitous Computing

Specifications - (explanation)










Function typesPhD positions
Hours 38.0 hours per week
EducationUniversity Graduate
Job number V36.1290
Translationsen nl 
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Job description

The Department of Electrical Engineering covers the application of electrical phenomena with respect to energy transfer, telecommunication, and calculation and processing of information and the technology involved. Both hardware, in the form of electronic and/or optical circuits and accessories, and software, in the form of system software for electro-technical application, are the subject of study. Existing and new electrical and/or optical components and systems are analyzed, designed and realized. In addition, the maintenance of these systems is the subject of research, as is the relevance for society of electrical engineering and informatics.
The research group Activity and Context Recognition Technologies (ACTLab, www.actlab.ele.tue.nl) within the Signal Processing Systems section of TU Eindhoven invites talented candidates to apply, who interested in pursuing a 4-year PhD program. PhD positions are fully funded, some through prestigious Marie Curie fellowships. Research topics are in machine learning, artificial intelligence, and signal processing, with applications in personal healthcare, energy-efficient buildings, and smart environments that respond to occupant behaviour.

PhD candidate position 1: Remote patient monitoring and decision support.

Background: Physicians lack inside into patient status and disease trends when they diagnose patients from observations made during examination periods at the clinic only. Ubiquitous personal healthcare systems could help to monitor a patient's status (vital functions) and provide insight into daily activities that is needed to interpret the patients' vital data and disease trends. In particular, patients with a chronic condition that are continuously at risk for a worsening condition would benefit from technology that can regularly provide coaching support and objective feedback to healthcare providers. By using such personal monitoring solutions health risk could be reduced and the patient's comfort be increased.
Focus: This project will develop a modular embedded patient monitoring system allowing to capture physiological information (e.g., heart activity), physical activity, patient data, and environmental context. Signal processing and machine learning methods will be investigated to extract daily activity and behaviour information. This project will emphasise Chronic Obstructive Pulmonary Disease (COPD).Keywords: Ubiquitous computing, embedded systems, machine learning, biomedical engineering.

PhD candidate position 2: Ubiquitous on-body and ambient sensor network design and evaluation:

Background: Miniaturised sensing and signal processing systems are essential for intelligent ambient and on-body assistants. Such assistants are intended to help the user in accomplishing daily activities and tasks by providing information, e.g. on health state for chronic patients, physical performance in sports, and energy saving in buildings. In all situations, available resources in such networks are limited urging effective algorithm implementations.
Focus: This project will investigate distributed sensor network systems for rapid prototyping in ubiquitous environments with the aim to extract activity information. In particular, the project will consider the tradeoff between system performance and resource usage and ultra low-power algorithm operation. Different prototyping network installations will be implemented and deployed to evaluate solutions in buildings and in wearable systems.
Keywords: Artificial intelligence, distributed systems, control theory

PhD candidate position 3: Behaviour estimation to optimise energy consumption in public buildings.

Background: Many building installations are currently manually operated according to assumptions of how occupants utilise buildings. However, semi-public buildings (such as offices) have a very dynamic utilisation depending on the activity and behaviour of their occupants. Dynamically adapting building installations and appliances to occupant behaviour can profoundly save energy.
Focus: This project will investigate ambient activity and behaviour recognition concepts to optimise energy consumption using various ambient sensor modalities. Sensor systems and algorithms are developed to derive activity information, which is subsequently used to control lighting, ventilation and temperature, and different appliances.
Keywords: Activity recognition, distributed systems, sensor fusion, pattern analysis, control theory.
PhD candidate position 4: Daily Routine Monitoring using self-adaptive activity recognition.
Background: Mobile systems, such as smartphones could assist patients in many daily life situations by providing coaching and training functions. At the same time, these devices offer excellent sensing and processing resources and connectivity to further ambient and on-body  resources. With these options, daily routines can be identified to adapt assistance functions, e.g. to support healthy eating, regular fluid consumption, relaxation, etc. However, these activities and routines are variable and static models cannot sufficiently capture such behavioural concepts.
Focus: This project will investigate self-adaptive activity recognition schemes for patient assistance systems using smartphones. Multi-layer activity recognition models will be build that use sensor information to adapt its services and the underlying recognition models.
Keywords: Ubiquitous sensors, biomedical engineering, control theor

Requirements

Successful candidates will have a strong background in one or more of the following fields, through their study curricula and previous project experience, ideally evidenced by first publications: biomedical engineering, pattern recognition, artificial intelligence, control theory. In addition, experience in analysis (e.g. Matlab) and programming tools (e.g. Python, C++) are mandatory, electronic design expertise is beneficia

Conditions of employment

The appointment is for four years. The daily communication language of the group is English. As an employee of the university you will receive a competitive salary as well as excellent employment conditions (including excellent sport facilities and child care). A salary is offered starting at EUR 2042 per month (gross) in the first year and increasing up to EUR 2612 per month (gross) in the last year. Moreover 8% bonus share (holiday supplement) is provided annually. Assistance for finding accommodation can be given. The research must be concluded by writing a PhD thesis.  TU/e offers opportunities for personal development by developing your social and communication skills. We do this by offering every PhD student a series of courses that are part of the Proof program as an excellent addition to your scientific education.
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Additional information

For further information please contact Oliver Amft, o.amft@tue.nl or check www.sps.ele.tue.nl/members/O.Amft .

Application

If you are interested in this position, please upload a detailed curriculum vitae, an application letter motivating why the position (mention the number) and the proposed research is of interest to you and summarizing your views on the research area, a publication list, a copy of your best publication in English, course lists of your Masters and Bachelor programs (incl. grades), results of a recent English language test and the names of two references. In addition please indicate when you would be available to assume a position. The positions will be filled as soon as possible. The application can be send, all in electronic form, by using the  'Apply Now' button.
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More information about employer Eindhoven University of Technology (TU/e) on AcademicTransfer. Direct link to this job opening: www.academictransfer.com/7722

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