Katholieke Universiteit te Leuven (KU Leuven)

PhD position: Signal processing algorithm design for next-generation neuro-sensor technology

 
Published
WorkplaceLeuven, Flemish Region, Belgium
Category
Position
Occupation rate10%

Description

PhD position: Signal processing algorithm design for next-generation neuro-sensor technology

In the frame of an ERC project, we are looking for a motivated and mathematically-oriented engineering Ph.D. candidate with an interest in signal processing algorithm design and applications thereof in next-generation neuro-sensing technology

This job opening covers a research position at the STADIUS group of the Department of Electrical Engineering (ESAT) of KU Leuven (Belgium) for a Ph.D. candidate in the frame of the ERC project ’DISPATCH Neuro-sense’. https://www.kuleuven.be/english/research/EU/p/horizon2020/es/erc/dispatch-neuro-sense

It involves novel signal processing algorithm design for next-generation wearable neuro-technology and neuro-sensornetworks.  A specific focus is on the design of adaptive multi-channel neural signal processing algorithms, amenable to low-power distributed or parallellizable architectures with constrained energy resources.

The work will be performed within the research division STADIUS (’Stadius Centre for Dynamical Systems, Signal Processing, and Data Analytics’) at the Department of Electrical Engineering (ESAT) at KU Leuven, Europe’s most innovative university (Reuters, 2018). STADIUS’s major research objective is to contribute to the development of improved digital (control and signal processing) systems that incorporate advanced mathematical modeling techniques as a crucial new ingredient.

The work will be performed within the research division STADIUS (’Stadius Centre for Dynamical Systems, Signal Processing, and Data Analytics’) at the Department of Electrical Engineering (ESAT) at KU Leuven, Europe’s most innovative university (Reuters, 2018). STADIUS’s major research objective is to contribute to the development of improved digital (control and signal processing) systems that incorporate advanced mathematical modeling techniques as a crucial new ingredient.

Candidates must hold a Masters degree in Electrical or Computer Science Engineering (or equivalent) with excellent grades, and with a strong mathematically-oriented engineering background. A good knowledge in the following fields is deemed necessary:

  • Signals and systems
  • (Statistical) signal processing 
  • Analysis of random/stochastic signals (stationarity, power spectral density, estimation theory, Wiener, etc.) 


Additional research/educational experience in any of the following topics is a plus:

  • Proficient with (design of) EEG recording equipment
  • Experience with EEG or other electrophysiological neural signals
  • Brain-computer interfaces and neural decoding
  • Sensor array and multi-channel signal processing
  • Statistical signal processing
  • Electronics for biomedical instrumentation
  • (Convex) optimization theory 
  • Component analysis theory (PCA, ICA, IVA, CCA, ...)
  • Machine learning (deep or not)


Candidates should be motivated, independent, critical, and should have strong team-player skills. Excellent proficiency in the English language is also required, as well as good communication skills, both oral and written.

!!! Important note: motivation letters are free format except for 2 obligated paragraphs:

1) Reason(s) why I am interested in this particular position: [write reasons here]

2) Facts or examples that demonstrate that I satisfy the listed requirements: [write reasons here]

The application deadline is in July 2020, but earlier applications are encouraged and will be considered as soon as they are received.

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 [at] kuleuven[.]be.

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In your application, please refer to myScience.be and reference JobID 2793.

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