Modelling, optimization and predictive analysis of business processes
Business processes are present in every company on earth. However, in most cases,
they are not explicitly described, which makes their improvement very difficult.
If models were available for those processes, one could reason on them to better
understand, refactor, and optimize them. Indeed, business process optimization
has become a strategic activity in organizations because of its potential to
increase profit margins and reduce operational costs.
The goal of this PhD thesis is to provide modelling and automated analysis
techniques for enabling companies to master the complexity of their internal
processes and for optimizing those processes with the final goal of improving
the quality and productivity of their businesses. More precisely, we plan first
to rely on and extend existing workflow-based languages (such as BPMN) to
capture the whole expressiveness of factory constituents with a specific focus
on quantitative aspects (execution times, probabilities, resources, energy
consumption, …). Second, we will develop analysis techniques to verify
properties of interest on these models (average execution times, resource usage,
time between failures, recovery times, costs, etc.) with the final goal of
proposing optimization plans for reducing costs and augmenting reliability and
profits. We also plan to develop predictive monitoring techniques for observing
and reasoning on execution traces, which would allow companies to improve their
processes without having at their disposal an explicit description of their
processes. All these solutions will be implemented in a tool, which will be
validated on real-world processes provided by the Soitec company.
# Required skills and profile:
– Knowledge of information/data models and business processes (BPMN)
– Knowledge of formal methods (concurrency theory) and verification is a plus
– Candidates who enjoy programming would be appreciated, as the work will include software development
– Education: MSc/Master 2 Recherche in Computer Science
– Good command of English as the working language, French is a plus
– Proven communication and interpersonal relationship skills, attention to detail, methodical approach, autonomy, team player
# Start date: Fall 2020
About 1800 EUR gross per month, health insurance included (French Social Security system).
The PhD thesis will take place within an academic/industrial collaboration
between University Grenoble Alpes and Soitec. The thesis location will be
mainly the Inria Montbonnot site, at about 10 kilometers from Grenoble.
# Application content
– Letter of application
– Curriculum vitae
– School report
– References or letters of recommendation, if any
– Scientific or technical publications, if any
# Application submission
Applications should be addressed directly to Ylies Falcone and Gwen Salaün,
by e-mail. Applications received after July 31, 2020 might not be considered
if a candidate has been selected already.
Ylies Falcone: email@example.com
Gwen Salaün: firstname.lastname@example.org