University of Luxembourg

Research Associate (Postdoc) in Applied Machine Learning (M/F)

University of Luxembourg

Luxembourg, Luxembourg

Organization

The University of Luxembourg is a multilingual, international research University.
The University of Luxembourg is seeking to hire a highly motivated and an outstanding researcher in the area of Applied Machine Learning (ML) for its Interdisciplinary Centre of Security and Trust (SnT), within the Signal Processing and Communications (SigCom) research group, led by Prof. Björn Ottersten and Dr. Symeon Chatzinotas.

SnT carries out interdisciplinary research in secure, reliable and trustworthy ICT (Information and Communication Technologies) systems and services, often in collaboration with governmental, industrial and international partners. SnT is active in several national projects funded by National Research Fund (FNR) and local industries, and international research projects funded by the EU FP7 programme, H2020 programme and the European Space Agency (ESA). For further information, you may check: www.securityandtrust.lu.

The SigCom research group carries out research activities in the areas of signal processing for wireless communication systems including satellite communications and radar systems, and signal, image and data processing aspects of computer vision, and is currently expanding its research activities in exploring low-complexity machine learning-assisted solutions to address several challenging problems in the aforementioned domains. For details, you may refer to the following:https://wwwen.uni.lu/snt/research/sigcom.

Your Role

The successful candidate is expected to perform the following tasks:

  • Contributing to expand group’s fundamental research on Machine Learning (ML)
  • Employing ML algorithms/tools for data analysis across various disciplines including wireless communications, radar systems and image processing
  • Coordinating research projects and preparing project deliverables
  • Disseminating results through scientific publications and conferences
  • Preparing new research proposals to attract industrial, national and European projects
  • Providing assistance in the supervision of PhD students
    For further information, please contact: Dr. Shree Krishna Sharma (shree.sharma@uni.lu).
    Your Profile
    • A PhD degree in Machine/deep learning or closely related field in Electrical Engineering, Computer Science or Applied Mathematics.
  • Expertise in one or more ML techniques including supervised, unsupervised, reinforcement learning, active learning, transfer learning and distributed learning.
  • Strong knowledge of various deep learning techniques including recurrent Neural Networks (NNs), convolutional NNs and recursive NNs.
    • Experience of working with one or more of the following ML and data analytics tools/frameworks: TensorFlow, MXNet, Caffe, PyTorch, Keras and GreyCat.
  • Sound publication track record on various ML techniques, data analytics, data mining or optimization methods.
  • Strong programming skills in Python, R or C++ and MATLAB.
  • Prototyping experience with ML algorithms is highly desirable.
  • Experience of working with industrial projects and European projects related to ML and data analytics is preferable.
  • Highly committed, excellent team-worker, and strong critical thinking skills
  • Excellent written and verbal communication skills in English

We offer

The University offers an initial two-year employment contract, which may be extended up to five years. The University offers highly competitive salaries and is an equal opportunity employer.

Further Information

Applications, written in English should be submitted online and include:

  • Curriculum Vitae (including your contact address, work experience, publications)
  • Cover letter indicating the research area of interest and your motivation
  • A research statement (max 1 page)
  • Contact information for 3 referees

All qualified individuals are encouraged to apply.

Deadline for applications: 30th of June 2019

Early application is highly encouraged as the applications will be processed upon reception. Please apply ONLINE formally through the HR system.

Applications by email will not be considered.
Link: http://emea3.mrted.ly/25r6w

Please apply via recruiter’s website.

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