We are seeking a highly motivated postdoctoral researcher to join our interdisciplinary team focused on the conversion of lignocellulosic biomass, an abundant and complex renewable resource that holds great promises for sustainable production of biofuels, biochemicals, and biomaterials. The efficient and selective breakdown of the plant cell wall—which is primarily composed of cellulose, hemicellulose, and lignin—remains a major scientific and technological challenge due to its structural heterogeneity and recalcitrance. This project aims to deepen our understanding of the enzymatic hydrolysis process by conducting an integrated and comprehensive analysis of both the solid residues and liquid hydrolysates generated during
biomass pretreatment and enzymatic conversion.
To capture the full complexity of these heterogeneous systems, we employ a suite of cutting-edge analytical techniques including liquid and solid-state nuclear magnetic resonance (NMR), liquid chromatography coupled with high-resolution tandem mass spectrometry (LC-HRMS/MS), infrared spectroscopy, fluorescence spectroscopy, and laser desorption ionization Fourier transform ion cyclotron resonance mass spectrometry (LDI-FT ICR MS). Each technique provides unique and complementary insights into the molecular composition, structural features, and chemical transformations occurring in both phases. The resulting datasets are large, highly multidimensional, and complex, reflecting the intricate nature of biomass.

This postdoctoral position is part of the Amaretto project funded by the ANR through the Priority Research Program and Equipment (PEPR) B-BEST (Biomass, Biotechnology, Technologies for Green Chemistry and Renewable Energies).

A key innovation of this project is the integrated use of all these analytical data simultaneously, aiming to explore potential synergies between solid and liquid phase information that are often studied independently. The combination of these diverse data streams might reveal hidden correlations and interdependencies that can lead to a more holistic understanding of biomass reactivity. Advanced machine learning and chemometric approaches will be employed to identify robust molecular descriptors and predictive markers of enzymatic hydrolysis efficiency.

The successful candidate will have to elaborate this data-driven strategy to facilitate the discovery of key factors controlling reactivity, potentially guiding the design of more effective biomass conversion processes and tailored enzyme cocktails. All the analytical data is already available.

Occasional travel for a few days within France and internationally.

This position offers partial remote working arrangements.

The PhD must have been awarded no more than three years before the start of the postdoctoral contract.

Expected scientific profile

• Solid expertise in analytical chemistry, chemometrics, machine learning, or related disciplines
• Proven ability to handle and interpret large-scale, heterogeneous, and multidimensional analytical datasets
• Experience in the development or application of multivariate statistical and machine learning approaches
• Strong interest in extracting predictive markers, descriptors, and structure–reactivity relationships from complex datasets
• Familiarity with one or several advanced analytical platforms, including NMR, LC-MS, spectroscopy, or related techniques
• Ability to contribute to an integrative and data-driven research strategy
  Interest in research related to renewable carbon resources, biomass valorization, and sustainable conversion processes

Expected interpersonal and organizational skills

• Ability to work in an interdisciplinary and collaborative research environment
• Excellent written and oral communication skills
  Capacity to interact effectively with academic, institutional, and industrial partners
• Strong autonomy, initiative, and sense of responsibility
  Excellent organizational skills and ability to manage research activities efficiently
• Scientific rigor, analytical thinking, and problem-solving capacity

• Doctorat en sciences des données, sciences analytiques
• Bonne maitrise de l’anglais indispensable. Des connaissances de français seront appréciées.

For further information and to apply, please contact:

marion.lacoue-negre@ifpen.fr