AGC - Faculty position in Computational Metabolomics

Karlstad University; Occitanie; Sur place; EUR 20 000 - 40 000; Plein temps

Résumé du poste

An established academic institution is seeking a dynamic faculty member for a position in Computational Metabolomics. This role focuses on advancing research in metabolite profiling and metabolic network analysis, integrating cutting-edge techniques such as machine learning and bioinformatics. The successful candidate will contribute to interdisciplinary research programs, mentor students, and develop innovative computational tools for analyzing metabolic pathways. Join a vibrant research community dedicated to exploring the complexities of biological systems and their implications in health and sustainability. This is an exciting opportunity to make a significant impact in the field of metabolomics.

Qualifications

• Strong research record in metabolomics and cheminformatics.
• Outstanding publication record in computational metabolomics.

Responsabilités

• Supervising graduate students in metabolomics and bioinformatics.
• Developing collaborative research programs in integrative omics.

Connaissances

Metabolomics data analysis

Cheminformatics

Metabolic network modeling

Machine learning

Bioinformatics

Mathematical modeling

Teaching and mentoring

Formation

Ph.D. in Chemistry

Ph.D. in Computational Biology

Ph.D. in Bioinformatics

Ph.D. in Biochemistry

Ph.D. in Systems Biology

Description du poste

AGC - Faculty position in Computational Metabolomics

Mohammed VI Polytechnic University is an institution oriented towards applied research and innovation with a focus on Africa.

The AGC is seeking to appoint an Open rank Professor position in Computational Metabolomics to advance research in metabolite profiling, metabolic network analysis, and data-driven approaches for understanding biological and biochemical processes. Computational metabolomics plays a critical role in deciphering complex metabolic interactions in biological and environmental systems, with applications in health sciences, agriculture, biotechnology, and environmental sustainability.

The successful candidate will contribute to the development of computational tools for metabolomic data analysis, integrating machine learning, bioinformatics, and mathematical modeling to study metabolic pathways and their implications in health, disease, and ecosystem function. This position will support interdisciplinary research programs in fields such as microbiome science, systems biology, environmental biotechnology, and drug discovery.

Job Description

The selected candidate will join a vibrant, interdisciplinary research environment, working alongside experts in bioinformatics, molecular biology, environmental science, microbiology, and computational biology.

Qualifications & Requirements

1. A Ph.D. in Chemistry, Computational Biology, Bioinformatics, Biochemistry, Systems Biology, or a related field.
2. A strong research record in metabolomics data analysis, cheminformatics, or metabolic network modeling.
3. An outstanding publication record in computational metabolomics, NMR/mass spectrometry-based analysis, and/or AI-driven metabolic research.
4. Demonstrated potential for excellence in teaching and mentoring at undergraduate and graduate levels.
5. Proven ability to pursue an independent research program and engage in interdisciplinary collaboration.
6. Strong motivation to develop computational solutions for metabolic pathway analysis, biomarker discovery, and metabolic engineering.

Responsibilities:

1. Supervising graduate students and postdocs in the fields of metabolomics, organic chemistry, bioinformatics, synthetic biology.
2. Developing collaborative research programs in integrative omics, chemometrics for potential industrial application.
3. Supporting institutional research and academic programs and actively participating in institutional activities.

For more information, visit the AGC website: http://agc.um6p.ma or email: Pr. Mohamed Hijri at [e-mail]

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Job details

Title

AGC - Faculty position in Computational Metabolomics