Job offer

Universite de Normandie Caen/CNRS; France; Sur place; EUR 35 000 - 45 000; Plein temps

Résumé du poste

A leading research institution in France is seeking a highly motivated post-doctoral researcher with expertise in bioinformatics and genomics to analyze complex datasets related to fibrosing interstitial lung diseases. The role involves methodological development and collaboration with clinicians and scientists, requiring a PhD and experience in NGS data analysis. A curiosity-driven approach and ability to work autonomously within a team are essential. Applications should include a personal letter detailing research interests and motivation.

Qualifications

• PhD or equivalent in a relevant field.
• Solid experience in NGS data analysis required.
• Experience with variant calling tools such as GATK or Mutect2.

Responsabilités

• Conduct large-scale analysis of high-dimensional datasets.
• Perform methodological development and data analysis.
• Collaborate with clinicians and scientists on the research project.

Connaissances

Bioinformatics expertise

Next-generation sequencing (NGS) data analysis

Proficiency in R and/or Python

Experience with single-cell RNA-seq pipelines

Experience with variant calling tools

Scientific curiosity

Ability to interact with clinicians and biologists

Formation

PhD in bioinformatics, computational biology, genomics, data science, or related field

Description du poste

Organisation/Company Universite de Normandie Caen/CNRS Department Cyceron Research Field Medical sciences » Other Computer science » Other Researcher Profile Recognised Researcher (R2) Positions Postdoc Positions Country France Application Deadline 1 Aug 2026 - 00:00 (Europe/Paris) Type of Contract Temporary Job Status Full-time Offer Starting Date 2 Mar 2026 Is the job funded through the EU Research Framework Programme? Horizon Europe (other) Is the Job related to staff position within a Research Infrastructure? No

Offer Description

We are seeking a highly motivated post-doctoral researcher with strong expertise in bioinformatics and genomics to join our research program on fibrosing interstitial lung diseases (ILDs), with a particular focus on somatic mutation analysis.

The project involves large-scale analysis of high-dimensional datasets, including:

• single-cell and single-nucleus RNA sequencing
• spatial transcriptomics (e.g., 10x Genomics, Xenium)
• germline and somatic mutation data analysis

The successful candidate will play a key role in methodological development, single cell data analysis and somatic mutation identification, working closely with clinicians and scientists.

The success of this project relies on an active international collaboration involving several partners in France, Germany (Hannover) and in US (Yale University).

Where to apply

E-mail [e-mail]

Requirements

Research Field Computer science » Other Education Level PhD or equivalent

Skills/Qualifications

• PhD in bioinformatics, computational biology, genomics, data science, biostatistics, or biology related field
• solid experience in next-generation sequencing (NGS) data analysis
• proficiency in R and/or Python
• experience with single-cell RNA-seq or bulk RNA-seq pipelines
• experience with variant calling tools (e.g., GATK, Mutect2, Strelka, VarScan, DeepVariant)
• scientific curiosity and strong motivation
• autonomy and rigor
• ability to interact with clinicians and biologists

Specific Requirements

We are looking for a young researcher who is committed to the project, curious, independent and highly motivated to develop biocomputational skills in the field of respiratory research. In addition, the candidate should be able to work as part of a team on multidisciplinary projects.

Applications

A complete application must contain:

• A personal letter with a brief description of your research interests and a motivation for why you are applying, as well as your contact information

Languages

ENGLISH Level Basic

Research Field Computer science » OtherMedical sciences » MedicineBiological sciences » Biology Years of Research Experience 1 - 4