16/04/2021

Master thesis “Machine Learning Methods for Unmanned Aerial Vehicles Communications” - Security & Communication Technologies


  • ORGANISATION/COMPANY
    AIT Austrian Institute of Technology GmbH
  • RESEARCH FIELD
    Computer science
    EngineeringElectrical engineering
    TechnologyCommunication technology
    TechnologyElectrical technology
    TechnologyTelecommunications technology
  • RESEARCHER PROFILE
    First Stage Researcher (R1)
  • APPLICATION DEADLINE
    16/05/2021 21:00 - Europe/Brussels
  • LOCATION
    Austria › Vienna
  • TYPE OF CONTRACT
    Permanent
  • JOB STATUS
    Part-time
  • HOURS PER WEEK
    20
  • IS THE JOB RELATED TO STAFF POSITION WITHIN A RESEARCH INFRASTRUCTURE?
    Yes

OFFER DESCRIPTION

We are Austria’s largest research and technology organisation and an international player in applied research for innovative infrastructure solutions. This makes us a powerful development partner for industry and a top employer in the scientific community. Our Center for Digital Safety & Security in Vienna invites applications for a:

Master thesis “Machine Learning Methods for Unmanned Aerial Vehicles Communications” - Security & Communication Technologies

IMAGINE

  • The rapid development of unmanned aerial vehicles (UAVs) represents an asymmetrical threat situation as a potential means of attack, especially in urban areas. Therefore, UAV detection, tracking and control are an important public safety task. Due to the rapidly advancing complexity and robustness of the communication systems of UAVs, machine learning (ML) concepts need to be used to allow an efficient extraction of the UAV signal from surrounding interference, and to develop prediction algorithms for their frequency hopping modulation methods.
  • As part of this master thesis, you will deal intensively with the topic of machine learning methods for UAV communications.

ENGAGE

  • You will perform a literature research on ML techniques applied to wireless communications, and you will provide a critical review.
  • You will learn about the communication technology and frequency modulation methods used by UAVs.
  • You will simulate and investigate selected ML techniques applied to extract UAV signals from interference, and to predict their frequency hopping patterns.

ACHIEVE

  • You will implement the best performing algorithms in a hardware testbed using LabView, and then test and evaluate the results from real world experiments.
  • You gain experience in the area of reliable wireless machine-to-machine communication.
  • You train scientific working and discussion with scientific experts.

Your qualifications as an Ingenious Partner*:

  • Academic studies of electrical engineering, telecommunication engineering, computer science or similar
  • Solid knowledge in digital signal processing, linear systems, and modulation formats.
  • Good knowledge of LabView, hardware design, desirable MATLAB and Python
  • Ability to integrate in a multinational research team
  • High level of commitment and team spirit
  • Very good knowledge of either German or English (fluent in spoken and written)

What to expect:

EUR 1.073,70 gross per month for 20 hours/week based on the collective agreement. There will be additional company benefits. You will be part of our international Young AIT network. As a research institution, we are familiar with the supervision and execution of master theses and we are looking forward to supporting you accordingly.

At AIT, the promotion of women is important to us - that's why we are especially looking forward to applications from female students!

TOMORROW TODAY - WITH YOU?

Please submit your application documents online, including your CV, a short paragraph describing your motivation for the chosen topic and a transcript of your lectures taken: https://jobs.ait.ac.at/Job/150512

More Information

Map Information

Job Work Location Personal Assistance locations
Work location(s)
1 position(s) available at
AIT Austrian Institute of Technology
Austria
Vienna
1210
Giefinggasse 4

EURAXESS offer ID: 630033

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