PhD Position in Signal Processing for Privacy-Preserving Distributed Learning in IoT

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PhD Position in Signal Processing for Privacy-Preserving Distributed Learning in IoT

Norwegian University of Science and Technology (NTNU)
Country of Position: 
Contact Name: 
Stefan Werner
Subject Area: 
Signal Processing Theory and Methods
Signal Processing for Communications and Networking
Machine Learning for Signal Processing
Start Date: 
October 20, 2018
Expiration Date: 
November 06, 2018
Position Description: 

About the position:

We have a vacancy for a PhD Research Fellow position at the Department of Electronic Systems (IES). The PhD position is for up to 4 years with 25% work assignments for NTNU IES.

Job description:

The pervasion of the Internet of Things (IoT), which connects numerous sensors, actuators, appliances, vehicles etc., has a strong impact on the evolution of smarter and greener cities as well as on environmental monitoring. A basic tenet underlying all key functionalities of the IoT is situational awareness, i.e., the ability to capture events and derive accurate critical information for decision making that enable timely action in a heterogeneous and highly dynamic environment. This calls for an intelligent infrastructure that is autonomous, dependable, and resilient to natural or man-made disturbances. A critical component of such an infrastructure comprises myriads of information-gathering sensors deployed at many points of concern in the city. The sensors deployed in smart cities' IoT constitute critical data sources, on which the ensuing analytics and control actions depend. Those sensors are interconnected through the internet, forming an important part of IoT, and most likely powered only by batteries. To ensure that the sensors function effectively, we need to take a holistic approach to designing secure sensor networks with energy-efficient functional algorithms starting with sensing, followed by data processing and communication to ensure reliable decision making to enable timely actions that make possible long lasting secure and dependable functionality.

The PhD projects will be around developing and analyzing new efficient statistical learning algorithms for distributed inference to improve data quality, reliability, privacy and security of the physical-layer signals in IoT. The aim is to go beyond state-of-the-art solutions, embrace a secureby-design philosophy by exploiting additional information available at the physical layer, and take a holistic approach that starts with smart sensors, smart inference, and secure two-way communication among all the devices in the network, and energy-efficiency.

We seek a highly-motivated individual who has

  • strong mathematical background and a research-oriented master thesis in a related field, e.g., signal processing, information theory, statistical machine learning, applied mathematics, or optimization
  • experience with programming

Publication activities in the aforementioned disciplines will be considered an advantage but is not a requirement.

Qualification requirements:

The qualification requirement is completion of a master’s degree or second degree (equivalent to 120 credits) with a strong academic background in Electrical Engineering, Applied Mathematics, Computer Science, or other relevant disciplines with a grade of B or better in terms of NTNU’s grading scale. Applicants with no letter grades from previous studies must have an equally good academic foundation. Applicants who are unable to meet these criteria may be considered only if they can document that they are particularly suitable candidates for education leading to a PhD degree.


For more detailed information and application submission, please visit the following link:

The submission should contain an application letter describing your motivation, relevant experience, skills and qualifications, and a brief research vision for the position (maximum 2 pages) along with a CV, publication list, MSc thesis, letters of reference, proof of fluency in the English language (if applicable) and grade transcripts from both bachelor and master’s degrees.

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