Senior Computational Biologist - Multi-Omics

CT19; Paris; Sur place; EUR 90 000 - 130 000; Plein temps

Résumé du poste

CT19 in Paris seeks a PhD-level scientist to lead the interpretation of complex microbial multi-omics data, translating raw genomic, transcriptomic and proteomic/metabolomic measurements into biological insights that guide machine learning models and experimental validation.

You will collaborate with an AI team to frame problems biologically, curate diverse datasets, and contribute to publications and IP while advancing cross-disciplinary research at the interface of biology and data science.

Qualifications

• PhD in microbial genomics, computational biology, bioinformatics or a closely related discipline.
• Significant experience analysing microbial multi-omics datasets, including genomics, transcriptomics and at least one of proteomics or metabolomics.
• Strong understanding of microbial genetics, evolution and the biological mechanisms underlying phenotypic variation.
• Experience integrating genomic data with experimental measurements to investigate genotype-phenotype relationships.
• Demonstrated ability to curate and interpret complex, heterogeneous biological datasets generated under real-world conditions.
• Familiarity with both reference-based and reference-free computational approaches where appropriate.
• Comfortable collaborating closely with machine learning researchers, contributing to feature representation, multimodal data integration and biological interpretation of AI models.

Responsabilités

• Build mechanistic understanding from genomic, transcriptomic and proteomic/metabolomic datasets, using multiple biological data layers to investigate complex microbial systems and inform experimental design.
• Work with paired genomic and experimental datasets to understand where sequence-based predictions align with observed biological behaviour, and where they diverge. Bring strong mechanistic understanding of microbial biology to interpret these differences.
• Curate and structure complex, heterogeneous biological datasets for machine learning, ensuring scientific quality while handling noisy, incomplete and real-world experimental data. Apply alternative computational approaches where traditional reference-based methods are insufficient.
• Collaborate closely with machine learning scientists to influence feature engineering, representation learning and multimodal data integration. Evaluate model outputs from a biological perspective and help distinguish statistically convincing results from biologically meaningful ones.
• Contribute to publications, conference presentations and intellectual property, while working in a highly interdisciplinary team spanning biology, machine learning and engineering.

Connaissances

Multi-omics analysis

Genomics

Transcriptomics

Proteomics

Metabolomics

Genotype-phenotype

Data curation

Bioinformatics

ML collaboration

AI model interpretation

Reference-based methods

Reference-free methods

Formation

PhD in microbial genomics, computational biology, bioinformatics or a closely related discipline

Description du poste

CT19 are supporting a long standing client with a key hire.

This is not a supporting computtaional role. You will lead the scientific interpretation of complex multi-omics datasets, working from raw genomic, transcriptomic and proteomic and/or metabolomic data through to biological insights that directly influence machine learning models and experimental validation.

You will work as a scientific partner to an experienced AI team, helping shape both the biological framing of problems and the interpretation of model outputs.

Key Responsibilities

• Build mechanistic understanding from genomic, transcriptomic and proteomic/metabolomic datasets, using multiple biological data layers to investigate complex microbial systems and inform experimental design.
• Work with paired genomic and experimental datasets to understand where sequence-based predictions align with observed biological behaviour, and where they diverge. Bring strong mechanistic understanding of microbial biology to interpret these differences.
• Curate and structure complex, heterogeneous biological datasets for machine learning, ensuring scientific quality while handling noisy, incomplete and real-world experimental data. Apply alternative computational approaches where traditional reference-based methods are insufficient.
• Collaborate closely with machine learning scientists to influence feature engineering, representation learning and multimodal data integration. Evaluate model outputs from a biological perspective and help distinguish statistically convincing results from biologically meaningful ones.
• Contribute to publications, conference presentations and intellectual property, while working in a highly interdisciplinary team spanning biology, machine learning and engineering.

About You

We're looking for someone who enjoys combining deep biological expertise with modern AI methods, takes ownership of their work and thrives in an ambitious, fast-moving research environment.

Requirements

• PhD in microbial genomics, computational biology, bioinformatics or a closely related discipline.
• Significant experience analysing microbial multi-omics datasets, including genomics, transcriptomics and at least one of proteomics or metabolomics.
• Strong understanding of microbial genetics, evolution and the biological mechanisms underlying phenotypic variation.
• Experience integrating genomic data with experimental measurements to investigate genotype-phenotype relationships.
• Demonstrated ability to curate and interpret complex, heterogeneous biological datasets generated under real-world conditions.
• Familiarity with both reference-based and reference-free computational approaches where appropriate.
• Comfortable collaborating closely with machine learning researchers, contributing to feature representation, multimodal data integration and biological interpretation of AI models.