2023-06320 - Post-Doctoral Research Visit F/M Multivariate Time Series Anomaly Detection

Contract type : Fixed-term contract

Level of qualifications required : PhD or equivalent

Fonction : Post-Doctoral Research Visit

About the research centre or Inria department

The Inria centre at Université Côte d'Azur includes 37 research teams and 8 support services. The centre's staff (about 500 people) is made up of scientists of different nationalities, engineers, technicians and administrative staff. The teams are mainly located on the university campuses of Sophia Antipolis and Nice as well as Montpellier, in close collaboration with research and higher education laboratories and establishments (Université Côte d'Azur, CNRS, INRAE, INSERM ...), but also with the regiona economic players.

With a presence in the fields of computational neuroscience and biology, data science and modeling, software engineering and certification, as well as collaborative robotics, the Inria Centre at Université Côte d'Azur  is a major player in terms of scientific excellence through its results and collaborations at both European and international levels.


Every year Inria International Relations Department has a few postdoctoral positions in order to support Inria international collaborations.

This year, postdoctoral positions within the frame of Inria London, Inria Brasil and Inria Chile programs and to strengthen partnerships with Simula (Norway), University of Waterloo (Canada) and KAIST and ETRI (South Korea) are eligible.

The postdoc contract will have a duration of 12 to 24 months. The default start date is November 1st, 2023 and not later than January, 1st 2024. The postdoctoral fellow will be recruited by one of the Inria Centers in France but it is recommended that the time is shared between France and the partner’s country (please note that the postdoctoral fellow has to start his/her  contract being in France and that the visits have to respect Inria rules for missions)

The postdoc position within the framework of a partnership

  • collaboration between the Zenith team and CEFET, Rio de Janeiro, in the context of the HPDASC Inria associated team in the Inria-Brasil partnership
  • see https://team.inria.fr/zenith/hpdasc

Is regular travel foreseen for this post ?

One trip a year from Zenith, France, to CEFET, Brasil (travel expenses will be covered within the limits of the two teams' budgets)



Assignments :
The recruited person will be assigned to the following project. 

Multivariate time series refers to a type of data that consists of multiple time series variables observed simultaneously over time. In other words, it consists of a set of observations over time such that each observation has multiple variables measured at the same time. This type of time series can arise in several application domains, such as medical monitoring, environmental sciences such as climate modeling, traffic flow prediction, finance, etc. One of the main challenges in analyzing multivariate time series data is the complexity of the data structure, as there can be many variables that interact with each other in complicated ways over time. This can make it difficult to identify patterns and relationships within the data, as well as to develop accurate prediction models. Anomalies in time series data are defined as patterns that do not conform to the expected behavior of the underlying application. The objective of this research direction is to find unusual events or behaviors in multivariate time series data. It can be useful, for example, to identify abnormal biodiversity phenomena, unusual changes in vegetation or animal populations, or anomalies in climate data. There are several existing methods for multivariate time series anomaly detection, including statistical methods like PCA and ARIMA, e.g., the well-known Matrix Profile problem. However, current MP tools cannot scale to very large time series databases.

For a better knowledge of the proposed research subject :
See all information about the research in Zenith here: https://team.inria.fr/zenith/ and in the HPDASC project here: https://team.inria.fr/zenith/hpdasc/

Collaboration :
The recruited person will be in connection with Prof. Esther Pacitti, Dr. Reza Akbarinia, Dr Florent Masseglia (Zenith team, Inria) and Prof. Eduardo Ogasawara (CEFET, Rio de Janeiro)

Responsibilities :
The person recruited is responsible for **** and will take initiatives for ****..

Steering/Management :
The person recruited will be in charge of designing and implementing solutions to Multivariate Time Series Anomaly Detection.

Main activities

Main activities :

1. Propose new parallel Matrix Profile  techniques based on massive distribution to enable anomaly detection from very large multivariate time series datasets.

2. Design an experimental platform to implement theses techniques based on the Spark framework.

3. Validate experimentally the proposed techniques using real datasets.

4. Write research papers and publish in top venues.

5. Present the results to the two teams and in international events.


The postdoc candidate should have a recent PhD in data science and the following skills:

  • Strong background in time series data analytics
  • Excellent research capabilities, as demonstrated by top research publications
  • Strong programming skills (Java, Scala, Python, R, C++, …) with modern data analytics frameworks such as Spark
  • Fluent English (written and spoken)

Benefits package

    • Subsidized meals
    • Partial reimbursement of public transport costs
    • Leave: 7 weeks of annual leave + 10 extra days off due to RTT (statutory reduction in working hours) + possibility of exceptional leave (sick children, moving home, etc.)
    • Possibility of teleworking and flexible organization of working hours
    • Professional equipment available (videoconferencing, loan of computer equipment, etc.)
    • Social, cultural and sports events and activities
    • Access to vocational training
    • Contribution to mutual insurance (subject to conditions)


Gross Salary: 2746 € per month