ORGANISATION/COMPANYInterdisciplinary Centre for Security, Reliability and Trust (SnT), University of Luxembourg
RESEARCH FIELDComputer scienceJuridical sciencesLiterature
RESEARCHER PROFILEFirst Stage Researcher (R1)Recognised Researcher (R2)Established Researcher (R3)Leading Researcher (R4)
APPLICATION DEADLINE13/07/2022 23:59 - Europe/Brussels
LOCATIONLuxembourg › Luxembourg
TYPE OF CONTRACTOther
Doctoral Researcher (PhD Candidate) on Machine Learning-based Predictive Maintenance in Industry 4.0
(Valid from 11/05/2022 to 13/07/2022)
Language: English (UK)
Organisation data: Interdisciplinary Centre of Security, Reliability and Trust
Job Number: UOL04980
Contract Type: Fixed Term Contract
Duration 36 Month
Schedule Type: Full Time
Work Hours 40.0 Hours per Week
Expected Start Date: 01/07/2022
Job (internal): Doctoral Researcher
Functions: PhD Candidates
About the SnT
SnT is a leading international research and innovation centre in secure, reliable and trustworthy ICT systems and services. We play an instrumental role in Luxembourg by fueling innovation through research partnerships with industry, boosting R&D investments leading to economic growth, and attracting highly qualified talent.
We’re looking for people driven by excellence, excited about innovation, and looking to make a difference. If this sounds like you, you’ve come to the right place!
Manufacturing companies in the Industry 4.0 era are increasingly looking for implementing Predictive Maintenance (PdM) to predict failures, classify faults, and optimize maintenance tasks. Artificial Intelligence (AI), and particularly Machine Learning (ML) techniques are applied to build such prediction, classification and optimization models. Although these models yield high performance in static scenarios, their performance becomes questionable during operational use due to two phenomena known as (i) “imbalanced data”: when predicting faults, the majority of the collected data/logs indicates a normal state of operation, whereas only a small fraction indicates faults; and (ii) model drifts (a.k.a, concept and data drifts): when changes happen within the statistical properties of the target class labels or within the independent features (e.g., due to perturbations resulting from a change in hardware, a defective sensor, a network malfunction, wireless interferences, etc.). While these two challenges (imbalance data & model drifts) have received significant attention in image recognition, they have been little investigated in the field of time series analysis and forecasting, which is at the heart of PdM. This PhD thesis will focus on investigating innovative approaches to progress the state-of-the-art in predictive maintenance (PdM) and Machine Learning (ML).
The PhD grant is funded by an industrial partnership program: SnT (University of Luxembourg) and Cebi (aworldwide manufacturer of electromechanical components for the automotive industry), so the PhD candidate must commit to collaborate with and support the industrial partner on a daily/weekly basis.
You Will Be Required To Perform The Following Tasks
* Survey the scientific literature in the relevant research domains
* Investigate new approaches to improve PdM performance when facing imbalanced data & model drifts situations/problems
* Disseminate results through scientific publications
* Communicate and closely work with the partner to collect requirements and report results
* Implement proof-of-concept software tools
The Supervision Team You Will Be Working With Is
* Sylvain Kubler: daily advisor
* Yves Le Traon: head of SerVal
Qualification: The candidate should possess a MSc degree (or equivalent) in Computer Science with strong programming skills.
Experience : The ideal candidate should have some knowledge and/or experience in a number of the following topics:
* Machine Learning and AI
* IoT and Industry 4.0
* Knowledgable in Elecrtronics and automation is a plus
* Strong software development skills are mandatory
Language Skills: Fluent written and verbal communication skills in English are required.
Here’s what awaits you at SnT
* A stimulating learning environment. Here post-docs and professors outnumber PhD students. That translates into access and close collaborations with some of the brightest ICT researchers, giving you solid guidance
* Exciting infrastructures and unique labs. At SnT’s two campuses, our researchers can take a walk on the moon at the LunaLab, build a nanosatellite, or help make autonomous vehicles even better
* The right place for IMPACT. SnT researchers engage in demand-driven projects. Through our Partnership Programme, we work on projects with more than 45 industry partners
* Multiple funding sources for your ideas. The University supports researchers to acquire funding from national, European and private sources
* Competitive salary package. The University offers a 12 month-salary package, over six weeks of paid time off, health insurance and subsidised living and eating
* Be part of a multicultural family. At SnT we have more than 60 nationalities. Throughout the year, we organise team-building events, networking activities and more
But wait, there’s more!
* Complete picture of the perks we offer<https://urldefense.com/v3/__https://wwwen.uni.lu/snt/join_us__;!!NLFGqXo...$>
* Discover our Partnership Programme<https://urldefense.com/v3/__https://wwwfr.uni.lu/snt/partnership_program...$>
* Download the brochure: Why choose SnT for your PhD?<https://urldefense.com/v3/__https://wwwen.uni.lu/content/download/130340...$>
Students can take advantage of several opportunities for growth and career development, from free language classes to career resources and extracurricular activities.
* Contract Type: Fixed Term Contract 36 Month (extendable up to 48 months if required)
* Work Hours: Full Time 40.0 Hours per Week
* Location: Kirchberg
* Employee and student status
* Job Reference: UOL04980
The yearly gross salary for every PhD at the UL is EUR 38.028,96 (full time)
Applications should be submitted online and include:
* Full CV, including list of publications and name (and email address, etc) of three referees
* Transcript of all modules and results from university-level courses taken
* Research statement and topics of particular interest to the candidate (300 words)
* Motivation letter
All qualified individuals are encouraged to apply.
Early application is highly encouraged, as the applications will be processed upon reception. Please apply formally through the HR system. Applications by email will not be considered.
The University of Luxembourg embraces inclusion and diversity as key values. We are fully committed to removing any discriminatory barrier related to gender, and not only, in recruitment and career progression of our staff.
About the University of Luxembourg
University of Luxembourg is an international research university with a distinctly multilingual and interdisciplinary character. The University was founded in 2003 and counts more than 6,700 students and more than 2,000 employees from around the world. The University’s faculties and interdisciplinary centres focus on research in the areas of Computer Science and ICT Security, Materials Science, European and International Law, Finance and Financial Innovation, Education, Contemporary and Digital History. In addition, the University focuses on cross-disciplinary research in the areas of Data Modelling and Simulation as well as Health and System Biomedicine. Times Higher Education ranks the University of Luxembourg #3 worldwide for its “international outlook,” #20 in the Young University Ranking 2021 and among the top 250 universities worldwide.
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