PhD Position (M/F) – Environmental Health Research in the Era of Large-Scale Data Circulation Strategies: Investigating the Uses of AI

Job Description

Thesis Subject

Presentation
Research in epidemiology is currently assumed to be undergoing a dual “data revolution.” On the one hand, this involves the collection and processing of massive datasets, defined by their volume, diversity, and heterogeneity (Leonelli, 2019). On the other hand, it concerns a redefinition of the issues surrounding data reuse. These questions are not new: indeed, general epidemiological cohorts — a flagship mechanism of public health research (Goldberg and Zins, 2012) — very early positioned themselves as research platforms producing a wide variety of data for multiple users. However, the associated challenges are now being framed in new terms, and they are particularly salient in the field of environmental health research, which relies heavily on cohort studies to understand the broad impacts of the environment on population health.
Regarding data production, environmental health researchers point to the emergence of a “digital epidemiology” (for example through the monitoring of exposure to pollutants using embedded sensors, but also through the use of data derived from social media or e-health applications) (Engelmann, 2022), serving the reconstruction of the totality of environmental exposures (defined as the exposome, Wild, 2005).
Furthermore, the issue of multiple data uses is currently undergoing major reconfiguration, to the point of becoming a significant driver of transformation in the health sector. Since at least the late 2000s, numerous public and private actors have advocated for the large-scale circulation of data in new usage contexts (e.g., Gagneux, 2009; Bras and Loth, 2013; Open Data in Health Commission, 2014), with the promise of improving population health (Inserm, 2022). This trend is reflected in recent national and European data reuse policies (EU Regulation 2025/327). The renewed focus on multiple uses poses new challenges for general epidemiological cohorts and environmental health research, such as the expansion of data-sharing ambitions, particularly within European networks, and reflections on interoperability with new databases.
Analytical methods based on artificial intelligence algorithms carry numerous promises regarding their capacity to profoundly transform research infrastructures such as general epidemiological cohorts in the era of large-scale data production and circulation policies. In particular, AI is expected to promote so-called precision public health (intervening in the right way, with the right population, at the right time) (Khoury et al., 2016), as well as to greatly expand the secondary use of health data (Villani, 2018; Ministry of Health, 2025). Yet, the implementation of AI in public health research has so far been much less studied than its use in healthcare settings (Amelang & Bauer, 2019).
The project carried out within the framework of this doctoral contract will focus specifically on infrastructures dedicated to environmental health research and on the effects generated by AI systems. How do current efforts to produce or aggregate massive datasets for multiple uses transform the way research is conducted within environmental health cohorts? What are the consequences for categories of knowledge and action in this field?
At the intersection of sociology of health and Science and Technology Studies, the recruited candidate will conduct an in-depth investigation within one or several environmental health cohorts using AI models in their work processes. The methods employed will primarily be qualitative and will address transformations in different set of practices, such as : data collection, data management, data quality assessment, processing through AI models (and the related ethical issues), large-scale data aggregation, networking within consortiums, and data dissemination to different user groups. In doing so, the research project will explore issues surrounding the production of evidence and knowledge in environmental health, and their translation into health recommendations, particularly in terms of prevention.
This doctoral research is part of the SandoSHS project (Health Data and Social Sciences [Données de santé et SHS], led by Quentin Dufour) and the AlgoCare project (MIAI Cluster research chair at Université Grenoble-Alpes, Algorithmic Care, environmental health work package [Soins Algorithmiques, WP santé environnementale] led by Séverine Louvel). Through this dual affiliation, the recruited candidate will benefit from a dynamic scientific environment and guaranteed access to fieldwork sites.

Desired Profile and Skills
Required:
- Master's degree (M2) in sociology, STS, or a related discipline (health anthropology, political science, history of science, history of health, management sciences, public health, etc.);
- Significant initial experience in qualitative research in the social sciences;
- Interest in interdisciplinary work;
- Fluency in both English and French, written and spoken.
Preferred:
- Experience conducting qualitative research in healthcare or public health settings.

Applications must include:
- An updated CV with contact information and mention of the completed or ongoing research dissertation;
- In a single PDF document: Academic transcripts for the Master's degree (M1 and M2 if available); A cover letter (maximum two pages) providing a concise explanation of how the candidate envisions positioning themselves with respect to the dissertation topic (fieldwork, data, methods, theoretical framework, etc.); A relevant academic text (e.g., dissertation or dissertation excerpt, fieldwork report);
- A letter of recommendation sent directly by email by the referee to the co-supervisors of the thesis, Quentin Dufour (quentin.dufour@cnrs.fr) and Séverine Louvel (severine.louvel@sciencespo-grenoble.fr).

Your Work Environment

- The recruited candidate will benefit from a 36-month doctoral contract, with a starting date planned for september 2026. The salary corresponds to the legal amount for doctoral contracts in France, i.e. €2,300 gross per month.
- Academic supervision will be provided by Quentin Dufour (CNRS, CNE, AMU) and Séverine Louvel (Sciences Po Grenoble-UGA and PACTE UMR CNRS).
- The recruited candidate will be enrolled in the ED355 doctoral school at Aix-Marseille Université.
- The candidate will be affiliated with the Centre Norbert Elias and PACTE. The person will benefit from access to the resources and facilities of both institutions.
- From a practical perspective, the workstation will be based in Grenoble, within the PACTE. The recruited candidate will therefore benefit from a scientific environment that fits the dissertation topic, including a research team specialized in digital health and environmental health issues (AlgoCare Chair), as well as a monthly seminar dedicated to transformations in digital health and health data.
- The PhD candidate will participate in collective events organized by both research centers (doctoral training sessions, research centers general assemblies, thematic workshops).
- Occasional travel between Grenoble and Marseille will be required, as well as travel related to fieldwork depending on its location. These trips will be funded through the SANDOSHS Chair.

Constraints and risks

No specific risks identified

Compensation and benefits

Compensation

2300 € gross monthly

Annual leave and RTT

44 jours

Remote Working practice and compensation

Pratique et indemnisation du TT

Transport

Prise en charge à 75% du coût et forfait mobilité durable jusqu’à 300€

About the offer

About the CNRS

The CNRS is a major player in fundamental research on a global scale. The CNRS is the only French organization active in all scientific fields. Its unique position as a multi-specialist allows it to bring together different disciplines to address the most important challenges of the contemporary world, in connection with the actors of change.

CNRS

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