Researcher in Dictionary Learning for SAXS Nanoparticles

Grenoble INP - Institute of Engineering; France; On-site; EUR 40,000 - 52,000; Full time

Job summary

Grenoble INP - Institute of Engineering invites applications for a Postdoc position focused on inferring internal structural parameters of lipid nanoparticles from SAXS data. The project emphasizes dictionary-based representations and parsimonious combinations to reconstruct experimental profiles while respecting physical constraints.

The candidate will develop dictionary-learning methods, explore semi-supervised learning, and work with interdisciplinary teams to ensure robust conclusions

Qualifications

• PhD in signal and image processing, statistical learning, AI, inverse problems or related fields.
• 8 years of higher education: PhD in a relevant field.
• Strong knowledge of signal and image processing, statistical learning and optimization.
• Experience solving inverse problems in scientific or experimental data.
• Proficiency in Python and scientific data analysis tools.
• Ability to develop and evaluate innovative methods in research.
• Ability to work independently and in an interdisciplinary environment.
• Strong scientific communication and paper writing skills.

Responsibilities

• Design dictionary-learning methods tailored to SAXS data for nanoparticle analysis.
• Develop representations decomposing scattering profiles into dictionaries of patterns.
• Incorporate physical constraints to ensure representations reflect lipid nanoparticle properties.
• Explore semi-supervised or self-supervised strategies with simulations and experiments.

Job description

Organisation/Company Grenoble INP - Institute of Engineering Department Engineering Research Field Engineering » Electrical engineering Researcher Profile First Stage Researcher (R1) Positions Postdoc Positions Application Deadline 12 Oct 2026 - 20:00 (Europe/Paris) Country France Type of Contract Temporary Job Status Full-time Offer Starting Date 2 Nov 2026 Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No

Offer Description

This research position aims at developing methods for inferring the internal structural parameters of lipid nanoparticles from small-angle X-ray scattering (SAXS) measurements, drawing on parsimony-based and dictionary learning approaches. The intrinsically ill-posed and non-unique nature of SAXS signal inversion, combined with the great diversity of morphologies observed, leads to the development of representations capable of decomposing the scattering profiles into
dictionaries of elementary patterns reflecting the different structural organisations of the nanoparticles. This approach will provide a naturally interpretable framework for linking dictionary-based representation to the structural characteristics of nanoparticles.

The core of the work will involve designing dictionary-learning methods tailored to SAXS data, enabling the automatic extraction of atoms representative of the scattering signatures and the reconstruction of experimental profiles using parsimonious combinations, whether linear or non-linear. Particular
attention will be paid to the integration of physical and biophysical constraints into the learning process, in order to
ensure that the representations obtained remain consistent with the known properties of lipid nanoparticles.

Strategies involving hierarchical, structured or multimodal dictionaries may, in particular, be investigated in order to represent
different scales of internal organisation.
Another major challenge lies in the limited availability of annotated data. The candidate will therefore explore semi-supervised or self-supervised dictionary learning strategies, combining numerical simulations, experimental data and a priori knowledge derived from the physics of diffusion. These approaches must
also be robust to measurement noise, instrumental variations and changes in domain induced by different pharmaceutical formulations, in order to ensure good generalisation ability across data acquired in a variety of contexts.

Where to apply

E-mail [e-mail]

Requirements

Research Field Engineering » Electrical engineering Education Level PhD or equivalent

Skills/Qualifications

Minimum qualification required: 8 years of higher education: PhD in signal and image processing / statistical learning / artificial intelligence / inverse problems or related fields.

Core competencies:

- Solid theoretical and practical knowledge of signal and image processing, statistical learning and optimisation methods

- Experience in developing and solving inverse problems, ideally in the context of scientific or experimental data

- Proficiency in scientific programming, particularly in Python, as well as in data analysis and processing tools

- Ability to develop, implement and evaluate innovative methods in a research context

- Ability to work independently on a scientific project, from problem formulation through to the validation of the approaches developed

- Ability to work in an interdisciplinary environment, interacting with experts from applied fields

- Skills in scientific communication, writing papers and presenting results