Researcher in Semi-Supervised Edge Energy Consumption Anomaly Detection and Classification
Catholic University of Leuven Department of Electrical Engineering
Belgium

The research group ESAT-WAVECORE of the Department of Electrical Engineering (ESAT) focuses on Wireless Communication. Within WAVECORE, the Networked Systems group covers research on several fields of wireless networking: Massive MIMO, UAV communications, mmWave, AI and Machine Learning, and Internet of Things. Networked Systems is looking for a suitable candidate to pursue a PhD in the topic of energetically self-sustainable beyond-5G mobile edge networks. In this PhD project, the aim is to combine expertise from ESAT-WAVECORE on 5G and AI, with various partners to tackle the huge energy consumption problem of computing and AI applications.

Project

The researcher will compare energy consumption models based on model and data- driven approaches and noisy run-time state observations. As prediction is in general more difficult than anomaly detection, (1) a first objective will be to determine normal and abnormal energy consumption profiles. Energy consumption anomalies could be hardware failures, as well as abnormal communication environment conditions (such as jammers of signal blockers). (2) A second objective will then be to label or classify the anomalies, first unsupervised and later with expert feedback. Expert feedback will be possible by training the model to work with interpretable features. (3) In a third objective, the normal energy consumption profiles will be used for energy consumption prediction. These models will work on the interpretable features, so ideally derived prediction models will be explainable to users and algorithm designers. Strong interaction with the other ESR working on resource optimization algorithms will be needed to understand how such algorithms work and where they can complement the learned models by this ESR.
 
Once hired, the candidate will: 
  • be working at KU Leuven, performing full-time research under the supervision of Prof. Sofie Pollin.
  • will be enrolled in the PhD program at KU Leuven, under the supervision of Prof. Sofie Pollin.
  • will additionally pursue two secondments at CEA and TNOR, for a respective duration of 5 and 5 months.

Profile

Required: At the time of recruitment, the applicant must not have lived in Belgium for more than 12 months in the previous 36 months (3 years).
Required: No more than 4 years spent in research/work activities after the achievement of the MS degree.
Preferred: A Master’s degree in Telecommunications, Computer Science, Data Science or equivalent.
Preferred: Very good communication skills in oral and written English.
Preferred: Background in wireless communication, performance modelling or optimization
Preferred: Open-mindedness, strong integration skills and team spirit.
Desired: good command of the Python programming language.
Desired: previous training MS-level training on machine learning techniques.

Offer

A competitive employment contract following the ITN rules will be provided: https://greenedge-itn.eu/phd-hiring-call/

Interested?

To apply, you must fill in the online application form here: https://guardiola.cttc.es/greenedge/form_papers.php
For more information please contact Prof. dr. ir. Sofie Pollin, tel.: +32 16 32 10 51, mail: sofie.pollin@kuleuven.be
You can apply for this job no later than May 31, 2021 via the
KU Leuven seeks to foster an environment where all talents can flourish, regardless of gender, age, cultural background, nationality or impairments. If you have any questions relating to accessibility or support, please contact us at diversiteit.HR@kuleuven.be.
  • Employment percentage: Voltijds
  • Location: Leuven
  • Apply before: May 31, 2021
  • Tags: Elektrotechniek


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