Professor – Biostatistics – Bioinformatics in Environmental Health

Grenoble, France

2026-04-02 (Europe/Paris)

Euraxess research field: Statistics, Computational mathematics, Biodiversity, Environmental science

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Expected skills:

Applicants must show a strong interest in teaching as well as a high-level scientific record in accordance with UGA’s ambitions. They must identify with UGA’s values, that is, being open to the world, emphasising ethics and scientific integrity, showing an interest in teamwork and being committed to the community. They should also have a sense of responsibility, in particular with regard to environmental and social issues.

Context and educational objectives:

Data science and artificial intelligence now play a key role in the fields of health and biotechnology. They have become essential tools, enriching and transforming traditional approaches rooted in biology, chemistry, and biophysics. In practice, they enable the acceleration of drug discovery, the optimization of clinical trials, the personalization of treatments, the improvement of diagnostics, and even the remote monitoring of patients through connected devices.

Mastering these approaches prepares students to meet the scientific and technological challenges of tomorrow. These developments call for a stronger integration of such training within our curricula.

In this context, the successful candidate will be responsible for leading the creation of a Bachelor’s degree in Artificial Intelligence for Health, which will be supported by an Engineering Master’s Track (CMI), with the first year (L1) scheduled to open in September 2026.

The successful candidate will also teach and take responsibilities across all study programs offered by the School of Pharmacy:

• The Pharmacy curriculum (year 2 to year 4),
• The Bachelor’s degree in Biotechnology for Health (year 1 to year 3), and
• The Master’s degree in Health Engineering (year 1 and year 2),

for which the teaching team is strongly engaged in the fields of biostatistics, mathematics, and computer science.

The successful candidate will coordinate and lead this teaching team, fostering cohesion and ensuring smooth communication among its members. The successful candidate will also organize and coordinate workshops as part of the thematic schools held by the School of Pharmacy.

The successful candidate will join the Grenoble Institute for Artificial Intelligence (MIAI), in which the School of Pharmacy is deeply involved. Innovative teaching methods promoting student engagement and active learning in this rapidly evolving field will be encouraged.

Finally, they will be integrated into Teaching Department 1: “Physico-chemical Sciences and Engineering Applied to Health”, and will contribute to the shared academic life of the School of Pharmacy.

Research project and scientific impact:

The successful candidate will be expected to develop multidisciplinary research at the intersection of health, ecology, and biostatistics. This position, firmly anchored in the “One Health” global approach, aims to shed light on the complex interactions between environmental dynamics, biological systems, and public health issues.

Through advanced probabilistic and statistical approaches, the successful candidate will integrate heterogeneous and multi-scale data, such as environmental, genetic, and clinical variables, with particular attention to the parsimony and efficiency of the methods used. The models developed will contribute to a better understanding of the impacts of climatic and anthropogenic changes on public health, by identifying significant correlations and key predictive trends.

Several aspects may be explored. Biodiversity plays a major role in ecosystem resilience: as a natural regulator, it influences human health dynamics. Innovative methodologies could be developed using tools such as DNA metabarcoding to quantify biodiversity and assess its impacts on ecosystems and health.

The study of links between biomarkers and genetic adaptation is also critical to understanding biological responses to environmental pressures. By integrating exposure, effect, and susceptibility biomarkers with genomic signatures, the evolutionary mechanisms underlying species and population resilience to environmental changes could be identified, while developing predictive indicators of vulnerability and health.

This research, positioned at the interface of ecology and health, will have concrete applications in the field of human health. Through innovative diagnostic tools that combine medical, genetic, and environmental data, the successful candidate will help address emerging health challenges. The use of artificial intelligence and machine learning techniques will strengthen these efforts, offering predictive and preventive solutions to complex pathologies linked to environmental changes.

By embracing this integrated and cross-disciplinary approach, the successful candidate will collaborate closely with experts in biostatistics, ecology, genomics, and public health, building bridges between fundamental research and real-world applications in health and ecology — thereby reinforcing the position of Université Grenoble Alpes and LECA as key players in the One Health domain.

Information for candidates :

• Université Grenoble Alpes recruits on the basis of skills and makes use of all talents. It encourages applicants with disabilities to apply for teaching and research positions.
• Senior lecturers and professors are required to reside at the place where they perform their duties (Art. 5 of Decree No. 84-431 of June 6, 1984).
• HRS4R certified and committed to an OTM-R (Open, Transparent, and Merit-based Recruitment) approach, the University of Grenoble Alpes recruits on the merits of candidates and in accordance with a transparent procedure.

Job details

Title
Professor – Biostatistics – Bioinformatics in Environmental Health

Employer
Université Grenoble Alpes

Location
621 avenue Centrale Grenoble, France

Published
2026-03-13

Application deadline
2026-04-02 16:00 (Europe/Paris)

Job type
Professor

Field
Biostatistics, Bioinformatics, Environmental Science, Computational Mathematics, Statistics,
Artificial Intelligence,
Pharmacy & Pharmacology,
Biotechnology,
Biomedical Engineering,
Physical Chemistry,
Ecology,
Systems Biology,
Public Health,
Probability Theory,
Genetics