Job Description

We are seeking a highly motivated scientist with deep expertise in gene regulation, particularly within non-coding regions of the genome, to join a multidisciplinary team focused on functional genomics and perturbation-driven discovery. The candidate will contribute to designing and executing experiments that interrogate transcriptional regulation and cellular responses to genetic/chemical perturbations, integrating both molecular and computational approaches.

This role operates within a dynamic, rapidly evolving research environment and requires a balance of experimental rigor, computational fluency, and strategic thinking aligned with broader programmatic goals.

Key Responsibilities

• Develop and apply perturbation-based approaches (e.g., CRISPR, small molecules) to characterize gene regulatory mechanisms.
• Generate and analyze gene expression datasets, including bulk and single-cell RNA sequencing.
• Integrate multi-omics data (e.g., transcriptomics, epigenomics) to infer regulatory networks and functional outcomes.
• Build or apply computational pipelines for differential expression analysis, regulatory inference, and pathway analysis.
• Collaborate closely with experimental and computational teams to translate findings into biological insights and hypotheses.
• Contribute to the development and optimization of novel technologies or workflows for gene regulation profiling (preferred but not required).
• Present findings internally and externally; contribute to manuscripts, reports, and grant applications.

Qualifications

• PhD in Genomics, Molecular Biology, Computational Biology, Bioinformatics, or a related field.
• Demonstrated experience in transcriptomic profiling, particularly in perturbation contexts.
• Proficiency in computational analysis (e.g., Python, R, or equivalent) for genomics data.
• Hands-on experience with molecular biology techniques (e.g., RNA-seq, CRISPR perturbations, library prep).
• Experience with single-cell genomics is highly desirable.
• Exposure to technology development, assay development, or platform building is a strong advantage.

We are seeking a highly motivated scientist with deep expertise in gene regulation, particularly within non-coding regions of the genome, to join a multidisciplinary team focused on functional genomics and perturbation-driven discovery. The candidate will contribute to designing and executing experiments that interrogate transcriptional regulation and cellular responses to genetic/chemical perturbations, integrating both molecular and computational approaches.

This role operates within a dynamic, rapidly evolving research environment and requires a balance of experimental rigor, computational fluency, and strategic thinking aligned with broader programmatic goals.

Key Responsibilities

• Develop and apply perturbation-based approaches (e.g., CRISPR, small molecules) to characterize gene regulatory mechanisms.
• Generate and analyze gene expression datasets, including bulk and single-cell RNA sequencing.
• Integrate multi-omics data (e.g., transcriptomics, epigenomics) to infer regulatory networks and functional outcomes.
• Build or apply computational pipelines for differential expression analysis, regulatory inference, and pathway analysis.
• Collaborate closely with experimental and computational teams to translate findings into biological insights and hypotheses.
• Contribute to the development and optimization of novel technologies or workflows for gene regulation profiling (preferred but not required).
• Present findings internally and externally; contribute to manuscripts, reports, and grant applications.

Qualifications

• PhD in Genomics, Molecular Biology, Computational Biology, Bioinformatics, or a related field.
• Demonstrated experience in transcriptomic profiling, particularly in perturbation contexts.
• Proficiency in computational analysis (e.g., Python, R, or equivalent) for genomics data.
• Hands-on experience with molecular biology techniques (e.g., RNA-seq, CRISPR perturbations, library prep).
• Experience with single-cell genomics is highly desirable.
• Exposure to technology development, assay development, or platform building is a strong advantage.