Assistant Scientist (Computational Biology) â Mzoughi lab â Oncoplasticity
Assistant Scientist (Computational Biology) â Mzoughi lab â Oncoplasticity
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1 Vacancy
Job Description
The Mzoughi LabOur lab focuses on understanding the epigenetic mechanisms driving phenotypic heterogeneity and plasticity in cancer.
Using cutting-edge multiomics technologies and lineage tracing we ONCOPLASTICITYMzoughi labexplore how tumor cells evolve and adapt during disease progression and under therapeutic pressures.
We are seeking a highly motivated Post-Doctoral Researcher/Scientist with a strong computational biology background to join our team. The successful candidate will apply and develop computational tools to analyze large-scale omics datasets including bulk and single-cell RNA-seq ATAC-seq and CUT&Tag and will contribute to studies elucidating tumor plasticity mechanisms. Experience in lineage tracing and single-cell data analysis is strongly preferred.
Responsibilities
- Analyze bulk and single-cell RNA-seq ATAC-seq and CUT&Tag datasets.
- Develop and optimize pipelines for integrating and interpreting multiomics data.
- Collaborate with both wet-lab and other dry-lab team members to generate hypotheses and refine experimental designs.
- Analyze lineage tracing datasets to uncover dynamic cellular transitions and clonal evolution.
- Present findings in lab meetings conferences and manuscripts.
Qualifications
- PhD in Biological Sciences or related field
- Two years experience
Preferred Qualifications
- Proficiency in programming languages such as R and Python with demonstrated experience in analyzing high-dimensional biological datasets.
- Experience with computational tools and packages for multiomics data analysis (e.g. Seurat Monocle edgeR DESeq2 ArchR).
- Familiarity with lineage tracing analysis methods and software is highly desirable.
- Strong skills in statistical modeling data visualization and interpretation of biological findings Familiarity with high-performance computing environments and version control systems (e.g. Git).
- Experience in analyzing single-cell epigenomics and transcriptomics datasets (e.g. single-cell RNA-seq and ATAC-seq).
- Knowledge of machine learning techniques for biological data analysis.
- Strong publication record in peer-reviewed journals.
Employment Type
Full-Time
