PhD in Testing Machine Learning Models and Self-Learning systems

Updated: about 1 month ago
Deadline: 30 Jun 2019

PhD in Testing Machine Learning Models and Self-Learning systems

  • Ref: 50013900
  • Fixed Term Contract up to 4 years in total, pending satisfaction of progress milestones (CDD)
  • Full time basis (40hrs/week)
  • Employee and student status

The Interdisciplinary Centre for Security, Reliability and Trust (SnT) invites applications from PhD candidates in the general area of Software Engineering. SnT is carrying out interdisciplinary research in secure, reliable and trustworthy ICT systems and services, often in collaboration with industrial, governmental or international partners.

The Centre is rapidly expanding its research activities and is seeking a highly motivated PhD candidate who wish to pursue research in close cooperation with our partners. For further information, you may check: https://wwwen.uni.lu/snt/research/serval

Position Description:

Full time PhD students are intended to work on the STELLAR project. The subject of the thesis will be “Definition and Measurement of Test Coverage Criteria in Machine-Learning Models” and “Test Case Generation for Machine Learning Models based on Disagreement Discovery”, which involve the development of effective techniques that can a) reliably measure and maintain the quality of training and test data that are used to build and evaluate ML models and b) generate test examples (similar to adversarial ones) that can improve confidence, identify issues and help securing the use of such ML models. In particular the key attributes to be investigated regard the definition of appropriate test criteria and test data generation methods. The successful PhD candidates will extensively explore and develop software engineering techniques that include the feasibility, practicality and success evaluation of prototype implementations.

The team you will be working with:

  • Yves Le Traon: Primary advisor
  • Maxime Cordy: Co-advisor
  • Mike Papadakis: Co-advisor

PhD Student Role: Under the direction of a professor, the candidate will carry out research activities and write a thesis with the main goal of obtain a PhD in the area of Software Engineering. This includes conducting literature surveys and establishing state-of-the-art; developing necessary experimental and simulation facilities where required; planning, executing, and analyzing experiments and simulations; conducting joint and independent research activities; contributing to project deliverables, milestones, demonstrations, and meetings; disseminating results at international scientific conferences/workshops and peer reviewed scientific publications.

Qualification:

  • Bachelor in Computer Science or related.
  • Master on a subject related to Informatics, or Software Engineering, or Computer Science, or Information Technology.
  • Strong background in program analysis and software engineering.
  • Strong programming and analytical skills.
  • Industry experience in information and communication technology will be considered as an advantage.
  • Commitment, team working, a critical mind, and motivation are skills that are more than welcome.
  • Optional: knowledge of machine learning, metaheuristics, statistics, and text analysis.

Language Skills: Fluent written and verbal communication skills in English are required.

We Offer:

The University offers a Ph.D. study program with a Fixed Term Contract up to 4 years in total, pending satisfaction of progress milestones (CDD), on full time basis (40hrs/week). The University offers highly competitive salaries and is an equal opportunity employer. You will work in an exciting international environment and will have the opportunity to participate in the development of a newly created university.

Application:

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

  • Curriculum Vitae (including your contact address, work experience, publications)
  • Cover letter indicating the research area of interest and your motivation
  • Transcript of all courses and results from the highest university-level courses taken
  • A short description of your Master’s work (max 1 page)
  • If possible, contact information for 3 referees

All qualified individuals are encouraged to apply.

Deadline for applications: June 30th, 2019. Early submission is encouraged, applications will be processed upon arrival.

Link: http://emea3.mrted.ly/26395


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