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Master thesis: Energy-attack mitigation using AI in intermittent computing systems

We are looking for dedicated master’s students to join us in the Connected Intelligence Unit at RISE.

The Connected Intelligence Unit is part of RISE Computer Science in Kista. The current research focus is on the Internet of Things and intermittent computing systems. Among the group's key technologies are the IoT network consisting of battery-less devices. The unit conducts projects together with industry and academic partners from Sweden and across the world.

Thesis Description
Dynamic application support in intermittent computing systems needs prediction of the system state based on the energy source and system parameters. An energy source under attack can jeopardize the application supported by the intermittent computing system. The focus of this project is to mitigate energy-attacks while providing reliable QoS support to applications in intermittent computing systems using artificial intelligence.


  • Start Time: As soon as possible
  • Scope: 30 hp
  • Location: RISE Computer Science, Kista, Stockholm

Who are you?
We expect you to have good programming skills and knowledge of intermittent computing systems. An interest in machine learning models and artificial intelligence is also a prerequisite.

Welcome with your application!
If this sounds interesting and you would like to know more, please contact Chetna Singhal (email: Applications should include a brief personal letter, CV, and recent grades. Candidates are encouraged to send in their application as soon as possible but at the latest on the 15th of December, 2022. Suitable applicants will be interviewed as applications are received. We do not accept applications via email.

Our union representatives are Lazaros Tsantaridis, SACO, 010 516 62 21 and Bertil Svensson, Unionen, 010-516 53 56.

Master thesis, Artificial intelligence, QoS, Application-aware, energy-attack, intermittent computing, RISE, Stockholm

About the position



Contract type

Temporary position 3-6 months

Job type

Student - Master Thesis/Internship

Contact person

Chetna Singhal

Reference number


Last application date


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