Research Fellow in Models and Algorithms for Information Diffusion on Graphs (DesCartes – WP2-8)

Job offer posted on 4 October 2022

DesCartes Program is looking for 1 year position (renewable) in Optimizationdriven hybrid AI.


The DesCartes programme is developing a hybrid AI, combining Learning, Knowledge and Reasoning, which has good properties (need for less resources and data, security, robustness, fairness, respect for privacy, ethics), and demonstrated on industrial applications of the smart city (digital energy, monitoring of structures, air traffic control).

The program brings together 80 permanent researchers (half from France, half from Singapore), with the support of large industrial groups (Thales SG, EDF SG, ESI group, CETIM Matcor, ARIA etc.).

The research will take place mainly in Singapore, at the premises of CNRS@CREATE, with a competitive salary and generous funding for missions.

Read more about the DesCartes program here.


The work will take place at the interface between the workpackages 2 and 8 of DesCartes:
Workpackage 2 focuses on fundamental research on learning from smart and complex data for hybrid AI. Standard machine learning needs big data and / or valuable feedback. However, data is often hard to get, expensive, incomplete, or accesslimited.
This is where the workpackage 2 starts off, as it focuses on learning decision-making policies from Hybrid Twins in complex / uncertain settings, where taking into account physics-based knowledge can reduce the amount of data and
computational resources needed.
Workpackage 8 aims at producing general methods on decision making frameworks based on Hybrid AI. The work will focus in particular on topics revolving around smart sensing, understood in the sense of optimal generation of data from sensors as well as interactions between data and physics-based models for handling missing and faulty measurements.

Goal of the Postdoctoral position: This postdoctoral research project will focus on problems pertaining to information diffusion in graphs. It has been argued that graphs are in many ways the main modality for the data we receive from the world, allowing us to solve complex problems in social networks, electrical grids, transportation or communication networks, etc. Consequently, graph-based learning is crucial to AI in general, as to AI in critical urban systems.
In particular, we will study models that can describe the propagation of information, understood in a large sense (e.g., faults or voltage sags in an electrical grid). Such models may bring together principles from viral diffusion models and physics-based notions and mechanisms. Based on such models, we will also study algorithms for computing spread in a given network, for instance in order to mitigate risks in grid management.
We will also revisit problems such as dynamic influence maximization with robustness guarantees in a dynamic diffusion graph, which may correspond to the communication medium between a fleet of drones. In this context, we will study once again physicsinformed diffusion models, now in a time-dependent formulation. Based on them, a key goal would be to model and solve the problem of maintaining a set of seed nodes in the dynamically evolving network, with the goal of maximizing the expected influence spread (or minimizing the expected delay for a spread to reach a certain proportion of nodes), while minimizing the amortized updating costs. For one example application scenario, we could cite here the one of maintaining an overlay of “super-drones” that can locate themselves and can also communicate with “basic drones” scattered in the city, in order to assist them in maintaining key information (location, status, requirements, etc).


    A successful candidate will have a PhD degree in computer science or applied mathematics, having a good knowledge of graph theory, optimization, machine learning and statistics. The candidate is expected to have strong programming skills, be highly motivated and creative.

    Funding: This Postdoc will be funded by DesCartes (A CREATE Programme on AI based Decision making in Critical Urban Systems), a hybrid AI project between CNRS and Singapore. It will be co-supervised between researchers from the National University of Singapore (NUS), Nanyang Technological University (NTU), TU Eindhoven and Paris-Saclay University.


      1. artificial intelligence
        2. data mining
        3. engineering
        4. computing platform
        5. data communication and management
        6. simulation


        Salary range: 70K to 85K SGD per year, depending on suitability and experience.

        Workplace address: CREATE Campus, CREATE Tower, 1 Create Way #08-01 Singapore 138602

        Interested applicants please send your resume to:

        – Please attach your full CV, with the names and contacts (including email addresses) of two referees.

        Check out this offer on MyCareersFuture website here.