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PhD/Teaching Assistant Position Machine Learning/Statistical Methods and Information Systems

ABG-79750 Sujet de Thèse
08/08/2018 > 25 et < 35 K€ brut annuel
QuantOM, HEC Liège, Université de Liège
Liège - Belgique
PhD/Teaching Assistant Position Machine Learning/Statistical Methods and Information Systems
  • Informatique
  • Economie et gestion
Information Systems, Machine Learning, Natural Language Processing, Systems Modelling, Business Process Modelling

Description

HEC Liège, the Management School of the University of Liège (Belgium) has an opening for a PhD/Teaching Assistant position in the domain of Machine learning/statistical methods applied to information systems.

Research

The PhD candidate will work on exciting research projects lying at the confluence of machine learning and information systems, including

  • Detection of incoherencies in technical specifications
  • Generation of domain models (class or ER or ER diagrams) from textual specifications
  • Modelling (formal) of business processes from textual descriptions.

Novel algorithms and techniques will be developed to support or fully automate the aforementioned applications.

The candidate will have the opportunity to work with top researchers at the junction of information systems, business analytics and machine (deep) learning, in a varied of applied, industrial projects as well as in theoretical research.

She/he will join a team of several PhDs within HEC Liège working on machine learning applied to various domains, such as medicine, marketing and finance. Furthermore, the candidate will be able to tap in a large network of international researchers, in the EU region and in Asia.

Teaching

In addition to research, the candidate will have the opportunity to actively participate in courses given at the bachelors’ and masters’ levels, covering domains such as

  • Information Systems (ERP systems, Business Process Modeling)
  • Business Analytics (introductory course to machine learning and statistics applied to business)
  • Text Analytics
  • Introductory maths courses (algebra, statistics and probabilities)
  • Operations Research

Overall, the candidate is expected to devote 40% of the time for teaching and 60% for research. More information on the teaching and research activities of the group.

Nature du financement

Financement public/privé

Précisions sur le financement

Présentation établissement et labo d'accueil

QuantOM, HEC Liège, Université de Liège

Supply chain management (SCM) and quantitative methods (QM) have been identified among the peaks of excellence at HEC Management School of the University of Liege. This distinction reflects the fact that the School offers a broad array of courses in these areas, leading in particular to a specialized master in Supply chain management, that a significant number of researchers carry out scientific projects related to SCM or QM, and that these projects are supported both by public research grants and by collaborations with industrial partners.

Most of the research activity in SCM and QM at HEC-ULg takes place within the framework of QuantOM, the Centre for Quantitative methods and Operations Management. More than a formal research institute, QuantOM is an open association of scientists who work under a common label in order to promote and to stimulate the development of research conducted at HEC-ULg (or more broadly, within the ULg) in the field of quantitative methods and of their applications to SCM, logistics, operations, and other areas of management and economics.

Some of the main areas of expertise and research within QuantOM are in transport management and transport economics, in production planning and scheduling, in the design and coordination of supply chains, in statistics and statistical process control, in integer programming and combinatorial mathematics, in business intelligence and analytics.

Profil du candidat

The suitable candidate should have

  • A recognized masters degree (typically 120 ECTS in Belgian universities) in business analytics information management, statistics, machine learning or general computer science
  • Programming skills, especially in Python (scikit-learn), Java, C++
  • Be fluent in English
  • Some knowledge of the French language (and if not, follow appropriate courses provided by the University

Date limite de candidature

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