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Position: Research Associate (Post-doc) on Automated Machine Learning (ML) in Industry 4.0 Factories
Institution: University of Luxembourg
Location: Luxembourg City, Luxembourg
Duties: Carrying out research in the predefined areas; Survey the scientific literature in the relevant research domains; Disseminating results through scientific publications; Communicate and closely work with the partner to collect requirements and report results; Implement the AutoML pipeline; Support PhD students in their daily research activities (incl., during experimental stages); Knowledgable in Elecrtronics and automation is a plus
Requirements: The candidate should possess a PhD degree (or equivalent) in Computer Science with strong programming skills. The ideal candidate should have some knowledge and/or experience in a number of the following topics: Software engineering; Machine learning and AI; Data science and statistics; IoT and Industry 4.0; Strong software development skills are mandatory
   
Text: UOL04973 31-Dec-2099 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. 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 possible model drifts (a.k.a, concept and data drifts), which occur 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. ). Overall, it is not an easy task to deploy and maintain ML algorithms in real-life environments; some studies having evidenced that putting a model from a research environment into production - where it eventually starts adding business value - takes from 8 to 90 days on average, and what is worse, up to 75% of ML projects never go beyond the experimental phase. To avoid this, reduce deployment and maintenance efforts, and facilitate collaboration between data science teams and IT professionals, an increasing interest for automated ML pipelines (aka AutoML pipeline) is observed. The objective of this Post-doc is to help the Cebi company, which is a worldwide manufacturer of electromechanical components for the automotive industry, household appliances and industrial applications, to design and set up such an AutoML pipeline. Within this context, the candidate must commit to support the industrial partnership on a daily/weekly basis, which implies supporting the PhD students who are working under this partnership with Cebi: the first PhD student focusing on the prediction of failures/faults using GAN (Generative Adversarial Networks)-like approaches; the second student focusing on developing innovative maintenance scheduling policies using Reinforcement Learning. Your role will be to contribute in the supervision of these two PhD students, which will lay the groundwork towards the design of the AutoML pipeline. Overall, you will have freedom to develop your own research agenda based on the company’s needs, while having a daily role in the management of the industrial partnership with the two PhD students. 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! Your Role... The Supervision Team You Will Be Working With Is Sylvain Kubler: daily advisor Yves Le Traon: head of SerVal You Will Be Required To Perform The Following Tasks Carrying out research in the predefined areas Survey the scientific literature in the relevant research domains Disseminating results through scientific publications Communicate and closely work with the partner to collect requirements and report results Implement the AutoML pipeline Support PhD students in their daily research activities (incl., during experimental stages) Knowledgable in Elecrtronics and automation is a plus Your Profile... Qualification: The candidate should possess a PhD 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: Software engineering Machine learning and AI Data science and statistics IoT and Industry 4.0 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... 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 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 Find out more about us! In Short... Contract Type: Fixed Term Contract 18 Month Work Hours: Full Time 40.0 Hours per Week Location: Kirchberg Job Reference: UOL04973 The yearly gross salary for every Postdoctoral Researcher at the UL is EUR 77.167,08 (full time) How to apply... Applications should 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 ONLINE 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. Further information For further information please contact us at sylvain.kubler@uni.lu or yves.letraon@uni.lu
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