PhD Student on Computer Vision Deep Learning Algorithms and Architectures (V36.3591)

PhD Student on Computer Vision Deep Learning Algorithms and Architectures

Aanstellingsfunctie(s)
Promovendus
Faculteit(en)
Faculteit Electrical Engineering
FTE
1,0
Sluitdatum
09/01/2019
Aanvraagnummer
V36.3591

The Electronic Systems Group of the Department of Electrical Engineering at Eindhoven University of Technology (TU/e) invites applications for a PhD position on Computer Vision Deep Learning Algorithms and Architectures.

Job description

Since the introduction of deep learning (DL) techniques, the advances in video analytics are going in a rapid pace.  Deep learning is based on neural networks comprising multiple layers of connected neurons that can be trained to classify input signals. In the domain of video analysis, this technique is used to detect, analyze, recognize, or classify objects. The deep neural network requires a tremendous amount of compute power and huge memory bandwidth. To satisfy these requirements advanced DL algorithms and architectures have to be developed which exploit parallel processing, in particular vector parallelism, specific accelerators, and advanced memory interfaces and memory hierarchy.

Within the ZERO program for Energy Autonomous Systems for Internet of Things, the PhD student will research techniques to efficiently map deep neural networks to various low energy consuming heterogeneous hardware and processing platforms, including GPUs, FPGAs and ASICs. The driver application, a smart surveillance camera system, will be delivered by one of the partners in the project (ViNotion, www.vinotion.nl).

Location
The research will be carried out in the Electronic Systems Group of Eindhoven University of Technology and in the research lab of ViNotion, also in Eindhoven.  The Electronic Systems (ES) group consists of seven full professors, one associate professor, nine assistant professors, several postdocs, about 40 PDEng and PhD candidates and support staff. The ES group is world-renowned for its design automation and embedded systems research. ViNotion is a leading company in the smart surveillance area.

The ideal candidate is pro-active, highly motivated, and independent, and has proven experience with the design of embedded systems. The candidate has also good written and oral communication skills in English. We are looking for candidates that match the following profile:

  • A master degree in Computer Science, Electrical Engineering or related disciplines with excellent grades.
  • Excellent knowledge of computer architectures.
  • Very good algorithmic skills, in particular with respect to vision and deep learning.
  • Very good programming skills (e.g., in C or C++) on CPUs and GPUs.
  • Experience with hardware design (e.g., in Verilog or HLS) and FPGAs.
  • A team player that enjoys working in multicultural teams.
  • Good communication and organization skills.
  • Excellent English language skills (writing and presenting).

We offer a fixed-term, 4 year position in a research group with an excellent reputation. Salary and benefits are in accordance with the Collective Labour Agreement for Dutch Universities, including:

  • A gross monthly salary between EUR 2266,- (first year) and EUR 2897- (last year).
  • Additionally, 8% holiday and 8.3% end-of-year annual supplements.
  • A minimum of 41 holidays per year (excluding bank holidays, for a full-time employment of 40 hrs/week)
  • Additional benefits, including excellent technical infrastructure, child care, holiday savings schemes, and sports facilities.
  • Assistance for finding accommodation is offered.
  • Personal development program aimed to develop your social and communication skills (see PROOF).

For more specific information about these positions please contact:
prof.dr. Henk Corporaal (H.Corporaal@tue.nl ), dr. ir. Sander Stuijk (S.Stuijk@tue.nl )

If interested, please use 'apply now'-button at the top of this page. You should upload the following:

  • a detailed curriculum vitae, a letter of motivation and portfolio with relevant work;
  • a cover letter explaining your motivation and suitability for the position;
  • a detailed Curriculum Vitae (including a list of publications and key achievements in research project(s));
  • contact information of two references;
  • copies of diplomas with course grades;

Candidates will be selected based on graduation mark and proficiency at university including consideration of the reputation of the university, relevant experience and skills, writing skills and publications, work experience as well as performance in relevant modeling exercises and interviews.

Please keep in mind; you can upload only 5 documents up to 2 MB each.