Missions

A 5-month Postdoctoral position is available in the SPARKS team at the I3S laboratory, Université Côte d'Azur, France.

The bioinformatics research conducted by the team focuses on the use of network approaches to analyze and integrate large-scale omics data and on the development of computational tools to model how disturbances in gene regulation can affect biological processes.

As part of a project to develop a new test to detect and quantify the risk of non-genotoxic carcinogens (NGTxC), we plan to take a holistic approach to better understand the processes at work at the biological system level. By integrating public data and genomic data generated by the biology laboratory with which we collaborate, we will model the data in the form of networks. In systems biology, a network maps molecular entities (e.g., genes, transcripts, proteins) via their functional interconnections (which can be physical interactions, transcriptional inductions, enzymatic activations, etc.). This modeling, which takes into account individual components and their interactions, will enable us to identify sub-parts of the networks that are particularly active in the dysregulations caused by NGTxC. Effective algorithms for discovering these active modules in complex networks have been proposed [1] and are being studied by the SPARKS team at I3S [2-3].

The mission of the recruited researcher will be to develop a method for identifying active gene modules based on single-cell analysis results.

References
1. Nguyen, H., Shrestha, S., Tran, D., Shafi, A., Draghici, S., & Nguyen, T. (2019). A comprehensive survey of tools and software for active subnetwork identification. Frontiers in genetics, 10, 155.
2. Corrêa, L., Pallez, D., Tichit, L., Soriani, O., & Pasquier, C. (2019, December). Population-based meta-heuristic for active modules identification. In Proceedings of the Tenth International Conference on Computational Systems-Biology and Bioinformatics (pp. 1-8).
3. Pasquier, C., Guerlais, V., Pallez, D., Rapetti-Mauss, R., & Soriani, O. (2021). Identification of active modules in interaction networks using node2vec network embedding. BioRxiv.
4. Rossetti, G., & Cazabet, R. (2018). Community discovery in dynamic networks: a survey. ACM computing surveys (CSUR), 51(2), 1-37.
5. Chunaev, P. (2020). Community detection in node-attributed social networks: a survey. Computer Science Review, 37, 100286.
6. Fu, L., Lin, P., Vasilakos, A. V., & Wang, S. (2020). An overview of recent multi-view clustering. Neurocomputing, 402, 148-161.
7. Ji, Y., Lotfollahi, M., Wolf, F. A., & Theis, F. J. (2021). Machine learning for perturbational single-cell omics. Cell Systems, 12(6), 522-537.

Informations complémentaires:
Applications must include :
- a cover letter, stating your motivation, scientific background, and research interests,
- a detailed CV with a list of publications,
- 2-3 references (name, institution, e-mail, telephone number, and relation to the candidate).

For more information, please contact Claude Pasquier (claude.pasquier@univ-cotedazur.fr)

Activités

Activities will include developing methodologies for applying community detection methods to single-cell data. A significant portion of the work will be devoted to implementation, including transforming all or part of the single-cell data to ensure compatibility with current approaches and adapting existing algorithms.

To do this, the recruited person will need to draw on recent developments in community detection in temporal networks [4], community detection in attributed networks [5], multi-view clustering [6], and, of course, machine learning applied to single-cell data analysis [7].
She will be responsible for the following tasks:
- reviewing the literature and software solutions to understand the underlying strategies for extracting knowledge from complex network structures,
- conducting practical tests to evaluate the effectiveness and ease of use of the selected tools in the context of high-throughput analysis,
- determining, implementing, and applying optimal strategies for data analysis,
- analyzing results in collaboration with biologists.

Compétences

- PhD in computational biology or computer science.
- Experience with the analysis of high-throughput 'omics data.
- Proficiency in programming (experience with Python and its ecosystem is preferred).
- Knowledge in single cell transcriptomics, gene regulation and network biology is desirable
- Experience with high performance computing is a plus.
- Ability to think and work independently, set goals and meet deadlines.
- Professional proficiency in English.
- Good communication and writing skills.
- Willingness to work in a multidisciplinary environment, sharing skills and ideas.

Contexte de travail

Located in the Sophia Antipolis technology park, the I3S laboratory (Computer Science, Signals and Systems of Sophia Antipolis - CNRS UMR 7271) employs 230 people, including about 100 researchers and professors and about 80 PhD students. The SPARKS team (Scalable and Pervasive softwARe and Knowledge Systems) is the largest team at I3S with a staff of 104, including 44 permanent staff.
The team studies the organization, representation and distributed processing of knowledge, as well as its extraction from data and its semantic formalization, with a particular focus on scaling up and designing adaptive knowledge-centric and human-centric software systems. The team is structured around four themes: "knowledge extraction and learning", "formalization and reasoning between users and models", "scalable software systems", and finallly the one that concerns us, "computer science and biology" (knowledge extraction, modeling and simulation of dynamic biological systems, formal proofs of the behavior of biological systems and computer-aided model-based reasoning).
The work will be carried out within the framework of the NewgenTOXiv project financed by the 4th Future Investment Programme (AIP 4) of the French government. The project involves 3 public research laboratories (I3S, IPMC and ICN) and two industrial companies (ImmunoSearch and NukkAI).

Contraintes et risques

The internal rules of the I3S laboratory apply.