Researcher

Karlstad University; Nancy; Sur place; EUR 40 000 - 60 000; Plein temps

Résumé du poste

A renowned academic institution in France is seeking a researcher to investigate cardiovascular failure. The candidate will analyze endothelial glycocalyx dynamics using omics data and develop predictive models for heart failure progression. The position requires a PhD, expertise in programming, and a strong background in biostatistics and machine learning. This is a full-time position for a duration of 24 months, offering competitive compensation based on experience.

Qualifications

• Knowledge of omics methods and their analytical pipelines.
• Foundations in biostatistics and epidemiology applied to clinical cohorts.
• Machine learning and artificial intelligence applied to biomedical data.

Responsabilités

• Analyze endothelial glycocalyx degradation in heart failure.
• Utilize clinical, biological, and proteomic data for understanding mechanisms.
• Develop predictive models for risk stratification in HFpEF.

Connaissances

Expertise in at least one programming language (R, Python, or SAS)

Experience in managing, processing, and integrating data

Expertise in advanced statistical analyses

Proficiency with database management tools (SQL, NoSQL)

Use of machine-learning libraries (scikit-learn)

Formation

PhD

Description du poste

LabAcute and Chronic Cardiovascular Failure (DCAC) (UMR_S 1116) / Multidisciplinary Clinical Investigation Center (CIC-P 1433)

Job Description

The recruited candidate will work on the alterations of the endothelial glycocalyx in heart failure with preserved ejection fraction (PMID: 40439171).

Main Missions

• Analyze the degradation of the endothelial glycocalyx at different stages of heart failure with preserved ejection fraction (HFpEF), using specific biomarkers (syndecan-1, hyaluronan, hyaluronidase) and correlating them with echocardiographic measurements of diastolic function.
• Utilize and integrate clinical, biological, and proteomic data from large cohorts and clinical trials (STANISLAS, HOMAGE, ALDO-DHF, MEDIA-DHF) to understand the pathophysiological mechanisms associated with glycocalyx alterations.
• Implement complex network modeling and artificial intelligence (machine learning) approaches to characterize the biological interactions involved in HFpEF progression.
• Assess the impact of preventive therapeutic strategies (in particular, the use of mineralocorticoid receptor antagonists, MRAs) on the evolution of the endothelial glycocalyx and the pathophysiology of HFpEF.
• Develop an integrated risk stratification model, combining biological phenotyping, proteomic data, and imaging, to identify individuals at highest risk of developing HFpEF and those most likely to benefit from personalized preventive treatment.

Activities

Collect, organize, and harmonize data from various cohorts and clinical trials (STANISLAS, HOMAGE, ALDO‑DHF, MEDIA‑DHF).

• Setup and execute bioinformatic pipelines for the analysis of omics data (particularly proteomics) and integrate them with clinical and imaging data.
• Develop and apply statistical and machine‑learning methods to explore the links between glycocalyx degradation and HFpEF progression.
• Build complex biological network models to identify the pathophysiological mechanisms involved.
• Assess the effects of preventive treatments (MRAs) on glycocalyx dynamics by integrating biological, proteomic, and clinical data.
• Design an integrated predictive model to identify individuals at highest risk of developing HFpEF and those likely to benefit from targeted prevention.
• Document and ensure the reproducibility and traceability of bioinformatics analyses (scripts, workflows, code repositories).
• Work closely with clinicians, biologists, and researchers to interpret results and guide analyses.
• Write reports, figures, and scientific communication materials (publications, presentations, conferences).

Position Specifics and Work Environment

• Work within the framework of an ANR‑funded project, at a Clinical Investigation Center, in a University Hospital, in contact with a multidisciplinary team of physicians, analysts, and basic researchers.
• Compliance with legal requirements related to health data.
• Adherence to the CIC‑P ISO9001 quality process.

Knowledge

• Omics methods (proteomics, transcriptomics, metabolomics) and their analytical pipelines.
• Foundations in biostatistics and epidemiology applied to large clinical cohorts.
• Machine learning and artificial intelligence applied to biomedical data (classification, clustering, predictive modeling).
• Basics in medical imaging (echocardiography, cardiac MRI) and multimodal data integration.

Skills

• Expertise in at least one scientific programming language: R, Python, or SAS.
• Experience in managing, processing, and integrating clinical, biological, and proteomic data.
• Expertise in advanced statistical analyses and biological network modeling.
• Use of machine‑learning libraries and frameworks (scikit‑learn).
• Proficiency with database management tools (SQL, NoSQL) and collaborative environments (Git/GitLab).

Abilities

• Scientific rigor and critical thinking in interpreting results.
• Ability to work in an interdisciplinary setting (clinicians, biologists, bioinformaticians).
• Strong communication skills (presentation of results, scientific outreach).
• Autonomy, organization, and respect for deadlines in a collaborative research environment.
• Scientific curiosity and interest in therapeutic innovation and precision medicine.

Experience

• Previous postdoctoral experience abroad is preferred.

Degree and Training Required

• PhD

Duration

24 months

Working time

• Full‑time
• Weekly working hours: 38h30
• Annual leave and RTT (reduction of working time)
• Contract staff: €2,895.57 gross/month with 0–2 years of experience, or €3,287.45 gross/month with 2–4 years of experience.

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