Postdoc in Chemistry & Artificiel Intelligence

Is the Job related to staff position within a Research Infrastructure?: No

Offer Description

Position description

Position identification

• Title of post: Postdoc in Chemistry & Artificiel Intelligence
• Type of contract: PostDoc
• Category (A,B or C): A
• Contract/project period: 12 months / 1 year
• Expected date of employment: 12/01/2026
• Proportion of work: 100%
• Workplace: UPR 22 – Institut Charles Sadron
• Desired level of education: PhD
• Experience required: at maximum 1 year after completion of PhD
• Contact(s) for information on the position: Dr. Alexis BIGO--SIMON, Assistant Professor, abigosimon@unistra.fr, +33 3 88 41 40 20
• Date of publication: 07/01/2026
• Closing date for the receipt of applications: 11/01/2026

Research project or operation

The PEPCAT’IA project aims to leverage deep learning using artificial intelligence (AI) to study the environment of enzymatic catalytic sites and transfer this knowledge to smaller self-assembled peptide sequences. This project has two main pillars: (1) understanding the 3D structure and chemical environment of enzymes for a broader range of activities than currently available through neural networks, and (2) transferring this knowledge onto short self-assembled peptide sequences. The innovation of PEPCAT’IA resides in its drive to explore a wider range of chemical reactions by generating new self-assembled catalytic peptide sequences.

Activities

Description of the research activities:

The recruited person will participate in data mining from existing literature and databases to extract relevant enzyme sequences for the project. Structural cleaning will also be performed to standardize the database, ensuring reliable results. Following this, the person will implement one or more deep learning methods to discover repetitive patterns within enzyme structures. These structural motifs will then be integrated into catalytic peptide sequences.

Related activities:

• Curating, validating, and refining enzymatic data from diverse sources (specialized databases, literature, internal datasets).
• Conducting a state-of-the-art review on 3D structure generation—especially those utilizing AI—and demonstrating its transferability to proteins, copolymers, and self-assembly systems.
• Developing and implementing a generative strategy for fabricating self-assembled structures that mimic an enzymatic catalytic site.
• Disseminating research findings through scientific publications and presentations.
• Establishing a data management plan within the framework of open science principles.

Skills

Qualifications/knowledge:

• Neural networks and Machine Learning (Autoencoders, Diffusion Models, Transformers).
• Data Science expertise (data collection, dataset analysis and cleaning, statistics, outlier handling).
• Expertise in chemical sciences / enzymology.
• Enzyme active sites (or catalytic pockets).
• Python scripting proficiency (NumPy, matplotlib, pandas, Biopython, etc.).
• Experience with PyTorch modules or PyTorch Lightning.
• Linux operating system proficiency.

Operational skills/expertise:

• Curating, analyzing, cleaning, and preparing an enzyme structural dataset to facilitate Machine Learning (ML) and Deep Learning (DL) applications.
• Performing extensive literature review to identify the most suitable neural network architectures that align with project objectives.
• Building, training, validating, and analyzing various deep learning models, including autoencoders, diffusion models, transformers, etc.
• Effectively communicating research findings both orally and in writing.
• Preparing code for deployment on a public institutional repository (e.g., GitLab).
• Drafting scholarly write-ups of results for various formats: scientific articles, conference presentations, and posters.

Personal qualities:

The successful candidate must be self-motivated, highly proactive, and capable of exercising initiative in all aspects of ongoing research. This role will take place within a multidisciplinary institute, where teamwork skills are absolutely essential.

Environment and context of work

Presentation of the laboratory/unity:

The Charles Sadron Institute (ICS) is a global leader in research across various fields related to polymers and self-assembled systems.

Located in Strasbourg/Kronenbourg, ICS functions as an "Unité Propre" (UPR 22) of the CNRS, associated with two key departments: CNRS Chemistry (primary affiliation) and CNRS Physics (secondary affiliation). The Institute also maintains strong partnerships with the University of Strasbourg and INSA Strasbourg, alongside close ties to the UFR of Physical and Engineering Sciences, ECPM (European School of Engineers in Chemistry, Polymers, and Materials), and the Faculty of Chemistry. ICS is a major contributor to both the Carnot Mica Institute and the Grand Est Materials and Nanoscience Research Federation, and it is involved in numerous national and international networks.

Structured into seven research teams and seven platforms, ICS brings together 53 researchers and academic staff, 38 engineers, technicians, and administrative personnel, as well as approximately 100 doctoral students, post-doctoral fellows, associate researchers, and interns.

Hierarchical relationship:

The direct supervisor of the hired individual will be Dr. Alexis Bigo-Simon, Assistant Professor at the University of Strasbourg.

To apply, please send your CV, cover letter and diploma to:

Dr. Alexis BIGO--SIMON, abigosimon@unistra.fr

Where to apply

E-mail: abigosimon@unistra.fr

Requirements

• Research Field: Information science
• Education Level: PhD or equivalent

Internal Application form(s) needed:

• Postdoc in Chemistry & Artificiel Intelligence.pdf (English, 122.27 KB - PDF)
• Post-Doctorat Chimie & Intelligence Artificielle.pdf (English, 122.95 KB - PDF)

Work Location(s)

• Number of offers available: 1
• Company/Institute: UPR 22 – Institut Charles Sadron
• Country: France

Contact

• City: Strasbourg
• Website: https://www.unistra.fr/
• Street: 4 rue Blaise Pascal
• Postal Code: 67000