Jobs – ULB – Postdoc in Data Science/Deep Learning/Big Data Mining for fraud detection

https://euraxess.ec.europa.eu/jobs/211909


  • ORGANISATION/COMPANY
    Université Libre de Bruxelles (ULB)
  • RESEARCH FIELD
    Computer science › Cybernetics
  • RESEARCHER PROFILE
    Recognised Researcher (R2)
  • APPLICATION DEADLINE
    01/06/2017 00:00 – Europe/Athens
  • LOCATION
    Belgium › Brussels
  • TYPE OF CONTRACT
    Temporary
  • JOB STATUS
    Full-time
  • HOURS PER WEEK
    40
  • OFFER STARTING DATE
    01/03/2018

Position Description

Applications are invited for full-time post-doctoral (3 years) research position at the Machine Learning Group, Computer Science Department, Université Libre de Bruxelles, Belgium.

The vacancy is available to be filled in February (earliest) till April (at the latest). The researcher is expected to carry out fundamental research in Data Science/Machine Learning/Big Data Mining and to take an active role in an applied research project on deep learning for credit card fraud detection which is funding the position.

The Machine Learning Group, ULB is a research group that carries out theoretical research on statistical machine learning, predictive modeling and computational statistics, and takes part in several applied research projects in domains as diverse as big data mining, bioinformatics to biomedical engineering and fraud detection.

Located in heart of Brussels, the Université Libre de Bruxelles is renowned for quality research through its prestigious affiliate research centers and world renowned faculty. Brussels, the capital of Belgium and the European Union, is a cosmopolitan city renowned for its picturesque squares and buildings, serene parks and gardens and a very vibrant cultural life spiced up by many international languages.

Deadline

Applicants are encouraged to apply as soon as possible as positions will remain open until filled by suitable candidates.

Contact

Candidates are invited to send their CV with the detailed list of publications, their PhD thesis and the name of at least 2 reference persons  in .pdf format by email to gbonte@ulb.ac.be.

The subject field of your email must include MLG-JOB: research position.

Equal Opportunity Employer

ULB is an equal opportunity employer and is committed to employing more handicapped individuals and especially encourages them to apply. ULB wishes to increase the proportion of women in areas in which they are underrepresented. Women are strongly encouraged to apply.

Web site for additional job details

Required Research Experiences

  • RESEARCH FIELD
    Computer science › Cybernetics
  • YEARS OF RESEARCH EXPERIENCE
    4 – 10

Offer Requirements

  • REQUIRED EDUCATION LEVEL
    Computer science: Master Degree or equivalent
  • REQUIRED LANGUAGES
    ENGLISH: Excellent

Skills/Qualifications

Applicants should have a recent PhD on a subject related to data mining, statistical machine learning or computational statistics. In particular, any research experience on data mining, machine learning, statistical analysis, or fraud detection will be an asset.

Applicants are expected to have strong programming skills, the ability to work independently, strong interpersonal skills, good writing and oral communication.

Upon offer of a position in MLG-ULB, the candidates who are non-EU citizens are required to obtain an appropriate long-term Schengen visa; further information is available from their nearest Belgian embassy.

Seminar- ULB – Context-sensitive Ordinal Regression Models for Human Facial Behaviour Analysis

depression-face_fear
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    ULB  Machine Learning Group (MLG)
             S E M I N A R
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Title:
       “Context-sensitive Ordinal Regression Models for Human Facial Behaviour Analysis”
When:
       Wed 8 July 2015 from 11:30
Where:
       Université libre de Bruxelles,
       Campus de la Plaine (http://www.ulb.ac.be/campus/plaine/plan.html)
       Département d’Informatique
       NO Building, Floor 8, local P.2NO8.08 (Rotule) (http://www.ulb.ac.be/campus/plaine/plan-NO.html)
       Boulevard du Triomphe – CP212
       1050 Bruxelles
Abstract:
Enabling computers to understand human facial behaviour has the potential to revolutionize many important areas such as clinical diagnosis, marketing, human computer interaction, and social robotics, to mention but a few. However, achieving this is challenging as human facial behaviour is a highly non-linear dynamic process driven by many internal and external factors, including ‘who’ the observed subject is, ‘what’ is his current task, and so on. All this makes the target problem highly context-sensitive, resulting in the changes of dynamics of human facial behaviour, which, in turn, is critical for interpretation and classification of target affective states (e.g., intensity levels of emotions or pain). In this talk, I will propose several extensions of the Conditional Ordinal Random Fields (CORF) model that are able to learn spatio-temporal and context-sensitive representations of human facial behaviour useful in various tasks of facial analysis. In particular, I will show how the proposed CORF models can be used for problems such as intensity estimation of facial expressions of emotion, intensity estimation of facial action units and facial expressions of pain. I will also demonstrate the performance of the models on the task of classification of facial expressions of persons with autism spectrum condition. Finally, I will discuss other potential applications of the models proposed and further challenges in modelling of human facial behaviour.
Speaker:
Ognjen Rudovic rreceived his PhD from Imperial College London, Computing Dept., UK, in 2014, a MSc degree in Computer Vision and Artificial Intelligence from Computer Vision Center (CVC), Barcelona, Spain, in 2008, and BSc in Automatic Control Theory from Electrical Engineering Dept., University Of Belgrade, Serbia, in 2006. He is currently working as a Research Fellow at the Computing Dept., Imperial College London, UK. His research interests include computer vision and machine learning, with a particular focus on face analysis, Bayesian learning and inference methods, and their application to human sensing. He is a member of Intelligent Behaviour Understanding Group (IBUG) at Imperial College London (http://ibug.doc.ic.ac.uk/people/orudovic).
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       MLG     http://www.ulb.ac.be/di/mlg/
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Pr. Gianluca Bontempi
co-Head of the Machine Learning Group
Département d’Informatique
Université Libre de Bruxelles
Boulevard du Triomphe – CP212
1050 Bruxelles, Belgium
email: gbonte@ulb.ac.be
Office Phone: +32-2-650 55 91
Fax: +32 2 650.56.09
mlg.ulb.ac.be
 
Director
Interuniversity Institute of Bioinformatics in Brussels (IB)²
ibsquare.be