Bridging the gap between biodiversity data and chemical pressure in aquatic environments by coupling non-targeted analysis with environmental DNA using digital tools


8 Feb 2024
Job Information

Research Field
Environmental science » Ecology
Chemistry » Biochemistry
Computer science » Database management
Researcher Profile
Recognised Researcher (R2)
Leading Researcher (R4)
First Stage Researcher (R1)
Established Researcher (R3)
Application Deadline
8 Mar 2024 – 22:00 (UTC)
Type of Contract
Job Status
Offer Starting Date
1 Oct 2024
Is the job funded through the EU Research Framework Programme?
Not funded by an EU programme
Is the Job related to staff position within a Research Infrastructure?

Offer Description

The quality of aquatic environments and water resources can be assessed from the perspective of chemical contamination, especially by emerging contaminants, or the biodiversity they host. The presence of contaminants is harmful to ecosystems, with effects on aquatic organisms and a loss of biodiversity (Santos et al. 2010; Hamilton et al. 2016; Reid et al. 2019; Alderton et al. 2021). To characterize this chemical contamination in aquatic environments, non-targeted analysis methods using high-resolution mass spectrometry (HRMS) have been developed (Aurich et al. 2023; Hollender et al. 2023). At Leesu, two PhD theses have been conducted on this topic, leading to the development of an analytical method (Huynh et al. 2021) and data processing tools (Sade et al. 2022), as well as the application of these methods to different urban waters to assess spatiotemporal variability of contamination. Concurrently, the study of environmental DNA (eDNA), where signals from macro- and microorganisms in the environment are retrieved from water samples, is increasingly used for monitoring the biodiversity of environments (Duarte et al. 2021; Altermatt et al. 2023). These methods can detect species as well as or even better than traditional biodiversity study methods, especially in aquatic environments that can integrate information about the entire watershed’s biodiversity (Altermatt et al. 2023; Bunholi et al. 2023; Reji Chacko et al. 2023).

Advances in technology that allow recording chemical fingerprints through non-targeted approaches or measuring eDNA for biodiversity studies provide enormous amounts of data. The challenge is to establish multivariate biological and chemical analysis tools to facilitate the use of this data (Slobodnik et al. 2019). Digital tools such as machine learning are increasingly used to process and interpret HRMS spectra of contaminants in water (Sade et al. 2022; Arturi and Hollender 2023; Hollender et al. 2023), as well as in the field of eDNA to extract biodiversity indicators or explain spatiotemporal variabilities (Cordier et al. 2018; Hu et al. 2023).

Currently, these two approaches have not been coupled. However, complex mixtures of chemicals must be considered along with their complex effects and impacts on ecosystems (Slobodnik et al. 2019). The coupling of the chemical footprint of contaminants and eDNA, which measures biodiversity, is a major challenge for monitoring the quality of receiving environments. Attempts to couple eDNA with hydrodynamics have been made (Warter et al. 2024) but studies coupling HRMS spectra and eDNA are currently rare (Reid et al. 2019; Sieber et al. 2023). The overall objective of this thesis project is to evaluate the possibility of jointly interpreting chemical fingerprints of contaminants in water with biodiversity information provided by eDNA by developing and applying numerical methods such as machine learning.


Project framework and partners

This PhD thesis is part of the Leesu’s research activities in collaboration with the MeSeine Innovation program led by the Public Service of Parisian Sanitation (SIAAP), one of whose objectives is to improve the monitoring of water bodies by combining different hydrological, chemical, and biological approaches. Regular sampling campaigns are carried out at 4 to 7 sites distributed between the Seine, the Marne, and the Oise.

The analysis of emerging contaminants relies on analytical instruments from the Prammics platform (OSU Efluve), especially liquid chromatography instruments (Waters Vion – UPLC-IMS-QTOF and Shimadzu HPLC Fraction collector). The UPLC-IMS-QTOF is an HRMS instrument equipped with ion mobility separation (IMS). eDNA will be measured by a service provider.




The objectives of the PhD thesis are as follows:

  • Identify for each type of data (HRMS and eDNA) tracers or indicators of interest from scientific literature and automate their processing.
  • Develop data processing methods to couple HRMS and eDNA data using advanced statistical or numerical tools such as machine learning.
  • Propose a relevant sampling strategy to acquire both types of data (HRMS and eDNA) on the MeSeine network and regularly monitor different sampling points.
  • Monitor and interpret the spatiotemporal fate of previously identified molecules/markers of interest (monitoring at different frequencies: seasonal, weekly, daily, and at different sampling points along the Seine river).

Funding category: Contrat doctoral

PHD title: Doctorat Sciences et Techniques de l’Environnement
PHD Country: France

Specific Requirements

Master's degree or engineering degree in bioinformatics/biostatistics, analytical chemistry, or environmental chemistry:

Skills in statistics (regression, discriminant automatic classification, decision trees…) and data analysis, machine learning
Programming skills, use of programming languages for data processing (R, Python…)
Skills in analytical chemistry (mass spectrometry, metabolomics) and/or molecular biology
Knowledge in environmental sciences (pollutants, water quality, and biodiversity concepts) is welcome
Proficiency in writing and a good level of English

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