Job Description

Postdoctoral Fellow in Bioinformatics - Chemoproteomics & Cancer Functional Genomics


Position Overview


A postdoctoral fellow position is available for a highly motivated scientist with expertise in bioinformatics, chemoproteomics, and functional genomics. The successful candidate will participate in research projects leveraging large-scale chemoproteomics datasets, integrating these with public cancer genomics resources, and mining CRISPR screening data to advance cancer biology and therapeutic discovery.


Key Responsibilities :

• Analyze and integrate high-dimensional chemoproteomics datasets with multi-omotic data (e.g., genomics, transcriptomics, proteomics) from public repositories such as DepMap and TCGA
• Develop, implement, and maintain robust computational pipelines in R and other programming languages (e.g., Python) for data processing, statistical analysis, and visualization
• Mine and interpret large-scale CRISPR screening datasets to identify novel cancer dependencies and therapeutic targets, utilizing resources such as DepMap and published CRISPR screens
• Collaborate with interdisciplinary teams of biologists, chemists, and clinicians to design and execute integrative studies, and contribute to the functional validation of computational predictions
• Present research findings at internal meetings and national/international conferences; publish results in high-impact journals
• Mentor junior researchers and contribute to grant writing and manuscript preparation

Required Qualifications:

• PhD in Bioinformatics, Computational Biology, Cancer Biology, Genomics, or a related field is required
• Demonstrated expertise in R and proficiency in other programming languages (e.g., Python)
• Experience working with and mining large public cancer genomics databases, such as DepMap and TCGA
• Proven track record in analyzing and integrating chemoproteomics and/or functional genomics datasets, especially CRISPR screening data
• Strong understanding of statistics and machine learning concepts as applied to biological data
• Excellent written and verbal communication skills, with a history of publishing in peer-reviewed journals
• Ability to work independently and collaboratively in a multidisciplinary research environment

Preferred Qualifications
• Experience with drug development and/or chemistry
• Familiarity with AI/ML approaches for biological data analysis.
• Prior experience in cancer research or translational bioinformatics


Application Instructions:

Interested applicants should submit a CV, a cover letter outlining research experience and interests, and contact information for three references. Applications will be reviewed on a rolling basis.

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