PostDoc Position in Model-driven Analytics and Machine learning at SnT University of Luxembourg, Department of Computer Science

The University of Luxembourg seeks to hire outstanding researchers at its Interdisciplinary Centre for Security, Reliability and Trust (SnT). The University of Luxembourg invites applications for a Research Associate position in model-driven analytics and machine learning at the Interdisciplinary Centre for Security, Reliability and Trust (SnT) within the SERVAL research group headed by Prof. Yves Le Traon. We are looking for highly motivated candidates who wish to pursue research that mixes model-driven engineering techniques with machine learning for performing efficient analytics (for instance on cyber-physical systems). Research Associate / PostDoc Fixed-term contract 2 years, full-time 40h/week (max 5 years) Ref.: R-STR-5009-00-B Number of positions: 1 Your Role The successful candidates will join a strong and motivated research team lead by Prof. Yves Le Traon in order to carry out research in the area of Software Testing. The position holder will be required to perform the following tasks: Shaping research directions and producing high quality scientific results Coordinating research projects and delivering outputs Being able to contribute in programming modules Providing guidance to PhD and MSc students Disseminating results through scientific publications Assisting in teaching duties For further information, please contact Yves Le Traon (yves.letraon@uni.lu) or Francois Fouquet (francois.fouquet@uni.lu) Keywords Software engineering, machine learning, OO programming, model-driven engineering. Your Profile The candidate should possess a PhD degree or equivalent in Computer Science, Information Systems or Software Engineering or Machine Learning. The ideal candidate should have software development skills and publication record in a number of the following topics: Programming Languages Software Engineering Model-driven Engineering Familiar with Machine learning and profiling Analytics techniques such as recommendation or decision support services Fluent written and verbal communication skills in English are essential. We offer The University offers a two-year employment contract, with highly competitive salaries, that may be extended for another three years. You will have the opportunity to work in an exciting international, collaborative and supportive environment that fosters personal and career development. The University is an equal opportunities employer and welcomes applications from all qualified and eligible candidates. Further Information The SerVal team SERVAL is a team of the Interdisciplinary Centre for Security, Reliability and Trust (SnT), and has about 20-25 researchers with expertizes on software engineering, software validation and security. The research interests of the group include (1) innovative testing and debugging techniques, (2) mobile security and reliability using static code analysis, machine learning techniques and, (3) model-driven engineering with a focus on IoT. The recent key-topics are related to Android malware detection and prevention using ML, and automatic debugging techniques such as automatic bug fixing but also Internet of things (IoT), Cyber-Physical Systems (CPS), Big Data and analytics leveraging model-driven techniques for distributed systems. Research Context Gaining profound insights from collected data of today’s application domains like IoT, cyber-physical systems, health care, or the ﬁnancial sector is business-critical. However, analyzing these data and turning it into valuable insights is a huge challenge. This is often not due to the large volume of data, e.g., collected from IoT sensors, but due to an incredibly high domain complexity, which makes it necessary to combine various extrapolation and prediction methods to understand the collected data. Model-driven analytics is a reﬁnement process of raw data driven by a model reﬂecting deep domain understanding, connecting data, domain knowledge, and learning. Model-driven analytics pursues the idea of model-driven engineering consistently further and brings it to another domain: data analytics. Furthermore, in certain ways it can be seen as an advancement of the models@run.time paradigm which promotes the usage of so-called runtime models, which reﬂect the state of a running system, to reason about its state. Similar to this paradigm, model-driven analytics suggests to use domain models as an abstraction which is simpler to handle than the reality. Whereas models@run.time abstract the state of complex cyber-physical systems, model-driven analytics abstract the expert knowledge of a certain domain in form of domain laws, mathematical formula, and learning rules in order to bring deep understanding to raw data. Therefore, it combines various areas of research, such as software engineering, machine learning, databases, big data, modelling, and analytics. A ﬁrst example are infrastructure monitoring systems, like used for the so-called smart grid, that manages our industrial partner, Creos Luxembourg S.A. Keywords Software engineering, model-driven engineering, predictive and prescriptive analytics, Big Data, Machine Learning, graph databases, metaheuristics, statistics, smart grid, cyber-physical system, IoT. In the SerVal team of SnT, you will be working with: Prof. Yves Le Traon: head of the team Dr. Francois Fouquet: research associate You may be tasked with contributing to industry-related projects, developing necessary experimental and simulation facilities where required; conducting joint and independent research activities; contributing to project deliverables, milestones, demonstrations, and meetings; disseminating results at international scientific journal/conferences/workshops and peer reviewed scientific publications. For inquiries please contact: Yves Le Traon, yves.letraon@uni.lu, Francois Fouquet (francois.fouquet@uni.lu) Further Information Application should be submitted online and include: Full CV, including: list of publications, 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). If possible a list of referees, or reference letters. Closing date: October 31, 2016. If you apply for this position please say you saw it on Computeroxy

The University of Luxembourg seeks to hire outstanding researchers at its Interdisciplinary Centre for Security, Reliability and Trust (SnT). The University of Luxembourg invites applications for a Research Associate position in model-driven analytics and machine learning at the Interdisciplinary Centre for Security, Reliability and Trust (SnT) within the SERVAL research group headed by Prof. Yves Le Traon. We are looking for highly motivated candidates who wish to pursue research that mixes model-driven engineering techniques with machine learning for performing efficient analytics (for instance on cyber-physical systems).

Research Associate / PostDoc
Fixed-term contract 2 years, full-time 40h/week (max 5 years)
Ref.: R-STR-5009-00-B
Number of positions: 1

Your Role

The successful candidates will join a strong and motivated research team lead by Prof. Yves Le Traon in order to carry out research in the area of Software Testing.
The position holder will be required to perform the following tasks:

Shaping research directions and producing high quality scientific results
Coordinating research projects and delivering outputs
Being able to contribute in programming modules
Providing guidance to PhD and MSc students
Disseminating results through scientific publications
Assisting in teaching duties

For further information, please contact Yves Le Traon (yves.letraon@uni.lu) or Francois Fouquet (francois.fouquet@uni.lu)

Keywords
Software engineering, machine learning, OO programming, model-driven engineering.

Your Profile

The candidate should possess a PhD degree or equivalent in Computer Science, Information Systems or Software Engineering or Machine Learning.
The ideal candidate should have software development skills and publication record in a number of the following topics:

Programming Languages
Software Engineering
Model-driven Engineering
Familiar with Machine learning and profiling
Analytics techniques such as recommendation or decision support services

Fluent written and verbal communication skills in English are essential.

We offer

The University offers a two-year employment contract, with highly competitive salaries, that may be extended for another three years. You will have the opportunity to work in an exciting international, collaborative and supportive environment that fosters personal and career development. The University is an equal opportunities employer and welcomes applications from all qualified and eligible candidates.

Further Information

The SerVal team
SERVAL is a team of the Interdisciplinary Centre for Security, Reliability and Trust (SnT), and has about 20-25 researchers with expertizes on software engineering, software validation and security.
The research interests of the group include (1) innovative testing and debugging techniques, (2) mobile security and reliability using static code analysis, machine learning techniques and, (3) model-driven engineering with a focus on IoT. The recent key-topics are related to Android malware detection and prevention using ML, and automatic debugging techniques such as automatic bug fixing but also Internet of things (IoT), Cyber-Physical Systems (CPS), Big Data and analytics leveraging model-driven techniques for distributed systems.
Research Context
Gaining profound insights from collected data of today’s application domains like IoT, cyber-physical systems, health care, or the ﬁnancial sector is business-critical. However, analyzing these data and turning it into valuable insights is a huge challenge. This is often not due to the large volume of data, e.g., collected from IoT sensors, but due to an incredibly high domain complexity, which makes it necessary to combine various extrapolation and prediction methods to understand the collected data. Model-driven analytics is a reﬁnement process of raw data driven by a model reﬂecting deep domain understanding, connecting data, domain knowledge, and learning.
Model-driven analytics pursues the idea of model-driven engineering consistently further and brings it to another domain: data analytics. Furthermore, in certain ways it can be seen as an advancement of the models@run.time paradigm which promotes the usage of so-called runtime models, which reﬂect the state of a running system, to reason about its state. Similar to this paradigm, model-driven analytics suggests to use domain models as an abstraction which is simpler to handle than the reality. Whereas models@run.time abstract the state of complex cyber-physical systems, model-driven analytics abstract the expert knowledge of a certain domain in form of domain laws, mathematical formula, and learning rules in order to bring deep understanding to raw data. Therefore, it combines various areas of research, such as software engineering, machine learning, databases, big data, modelling, and analytics. A ﬁrst example are infrastructure monitoring systems, like used for the so-called smart grid, that manages our industrial partner, Creos Luxembourg S.A.
Keywords
Software engineering, model-driven engineering, predictive and prescriptive analytics, Big Data, Machine Learning, graph databases, metaheuristics, statistics, smart grid, cyber-physical system, IoT.
In the SerVal team of SnT, you will be working with:

Prof. Yves Le Traon: head of the team
Dr. Francois Fouquet: research associate

You may be tasked with contributing to industry-related projects, developing necessary experimental and simulation facilities where required; conducting joint and independent research activities; contributing to project deliverables, milestones, demonstrations, and meetings; disseminating results at international scientific journal/conferences/workshops and peer reviewed scientific publications.

For inquiries please contact:
Yves Le Traon, yves.letraon@uni.lu, Francois Fouquet (francois.fouquet@uni.lu)

Further Information

Application should be submitted online and include:

Full CV, including: list of publications, 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).
If possible a list of referees, or reference letters.

Closing date: October 31, 2016.

If you apply for this position please say you saw it on Computeroxy