2 PhD positions in Distributed Learning in IoT with Adversarial Environments

You are here

Top Reasons to Join SPS Today!

1. IEEE Signal Processing Magazine
2. Signal Processing Digital Library*
3. Inside Signal Processing Newsletter
4. SPS Resource Center
5. Career advancement & recognition
6. Discounts on conferences and publications
7. Professional networking
8. Communities for students, young professionals, and women
9. Volunteer opportunities
10. Coming soon! PDH/CEU credits
Click here to learn more.

2 PhD positions in Distributed Learning in IoT with Adversarial Environments

NTNU - Norwegian University of Science and Technology
Country of Position: 
Contact Name: 
Stefan Werner
Subject Area: 
Signal Processing Theory and Methods
Signal Processing Communications and Networking
Machine Learning for Signal Processing
Start Date: 
April 05, 2019
Expiration Date: 
May 07, 2019
Position Description: 

We have 2 PhD Positions open in Distributed Statistical Learning in IoT with Adversarial Environments

Job Description

In the emerging paradigms of cyberphysical systems (CPS) and internet-of-things (IoT), large quantities of data are constantly collected by numerous sensors that are often geographically dispersed or, to make the matters even more challenging, mobile. This makes concentrating the data at a central processing hub unfeasible due to constraints imposed on the network infrastructure, e.g., the energy budgets of the sensors, as well as the capacity of the communication channels, and security requirements. Thus, distributed processing of data over networks of machines/agents is essential for solving inference problems related to many CPS/IoT-based applications.

Current methods for distributed inference and learning are not adapted for operation in IoT systems with malicious adversaries, vulnerable to data falsification and susceptible to privacy leakage. The goal of this project is to develop distributed signal processing and machine learning algorithms that preserve privacy and are resilient to malicious disturbances, while still maintaining the operational goal of the IoT system, i.e., to effectively convert the data collected by myriads of agents/sensors into actionable intelligence.

Qualification requirements

 We seek two highly-motivated individuals who have

  • strong background in mathematics and signal processing, and a research-oriented master thesis in a related field, e.g., statistical signal processing, information theory, statistical machine learning, multi-agent networked systems, applied mathematics, or optimization
  • experience with programming
  • good written and oral English language skills

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

Salary and conditions

PhD candidates are remunerated in code 1017, and are normally remunerated at gross from NOK 449 400 before tax per year. From the salary, 2 % is deducted as a contribution to the Norwegian Public Service Pension Fund.

Application submission

For more information on the application submission and a detailed list of required documents, please follow the link Distributed Learning in IoT with Adversarial Environments.

SPS on Twitter

  • THIS FRIDAY: Join our Vice President-Membership, K.V.S. Hari, and Membership Development Committee Chair, Arash Moh… https://t.co/rGSzhHAwgM
  • The SPACE webinar series continues tomorrow, Tuesday, 11 August at 11 AM ET with Dr. Xiao Xiang Zhu presenting "Dat… https://t.co/X5oz4KiJwX
  • now accepting submissions for special sessions, tutorials, and papers! The conference is set for June 2… https://t.co/sB3o5ItL0j
  • DEADLINE EXTENDED: The IEEE Journal of Selected Topics in Signal Processing is now accepting papers for a Special I… https://t.co/2SJwqj7aDB
  • NEW WEBINAR: Join us on Friday, 14 August at 11:00 AM ET for the 2021 SPS Membership Preview! Society leadership wi… https://t.co/1PLaZIt2VQ

SPS Videos

Signal Processing in Home Assistants


Multimedia Forensics

Careers in Signal Processing             


Under the Radar