PhD position with focus in Cybersecurity

8 Jan 2024
Job Information

Organisation/Company

Umeå university

Department

Computing Science

Research Field

Computer science » Computer systems

Researcher Profile

First Stage Researcher (R1)

Country

Sweden

Application Deadline

15 Jan 2024 - 11:59 (Europe/Stockholm)

Type of Contract

Temporary

Job Status

Full-time

Hours Per Week

40

Offer Starting Date

1 Apr 2024

Is the job funded through the EU Research Framework Programme?

Not funded by an EU programme

Is the Job related to staff position within a Research Infrastructure?

No

Offer Description
Project description and working tasks

The rapid development of autonomous systems, connected devices, and distributed applications poses several challenges in dealing with petabytes of data in diverse resource-constrained environments. Federated machine learning (FML) is a collaborative learning solution to handle these problems without sharing data with centralised servers. However, several emerging threats target FML training, learning, and inference to fail or mislead models at early learning rounds, particularly backdoor and bitflip attack and defence strategies under-explored in FML. These results jeopardize achieving trustworthy performance for any downstream tasks. Therefore, this project envisions developing and validating attack and defence strategies in federated learning for limited and diverse non-iid (independent identically distributed) data under non-standard and adversarial settings, which are ideally suited for edge AI infrastructures. These goals can be achieved by inducing unique features in federated learning algorithms such as robust training, model restoration, trustworthy device selection, secure learning and inference, fault-tolerance against failures and attacks, as well as resilient, fair and robust models. The ambition is to validate them in classical non-standard settings and apply them to solutions for constraint environments (e.g., the Internet of Things (IoT) and robotic arms). Potentially, teaching up to a maximum of 20% can be included in the work tasks.

The position is funded by The Knut and Alice Wallenberg Foundation through The Wallenberg AI, Autonomous Systems and Software Program (WASP) as part of the WASP faculty establishment package and has a unique opportunity to work with Cybersecurity Lab, Nanyang Technological University, Singapore. WASP is Sweden’s largest individual research program ever, a major national initiative for strategically motivated basic research, education and faculty recruitment. The program addresses research on artificial intelligence and autonomous systems acting in collaboration with humans, adapting to their environment through sensors, information and knowledge, and forming intelligent systems-of-systems. The vision of WASP is excellent research and competence in artificial intelligence, autonomous systems and software for the benefit of Swedish industry. Read more: https://wasp-sweden.org/

Requirements

Research Field

Computer science » Computer systems

Education Level

Master Degree or equivalent

Skills/Qualifications

Please look into the Advert for details. https://www.umu.se/en/work-with-us/open-positions/post-doctor-2-years-p…

Specific Requirements

Please look into the advert for details. https://www.umu.se/en/work-with-us/open-positions/post-doctor-2-years-p…

Languages

ENGLISH

Level

Excellent

Research Field

Computer science » Computer systems

Additional Information

Website for additional job details

https://www.umu.se/en/work-with-us/open-positions/post-doctor-2-years-position-…

Work Location(s)

Number of offers available

1

Company/Institute

Umea University

Country

Sweden

State/Province

Vasterbotten

City

Umea

Postal Code

90187

Street

MIT Huset

Where to apply

Website

https://umu.varbi.com/en/apply/positionquick/686074/?where=4

Contact

State/Province

Vasterbotten

City

Umeå

Website

http://www.cs.umu.se

Street

MIT Building

Postal Code

SE-901 87

E-Mail

[email protected]

Mobile Phone

+46764338237

STATUS: EXPIRED