Post-Doc in Computational Genomics and High-throughput Genome Editing

Job Description

We are seeking an enthusiastic and highly motivated Postdoctoral Researcher with a strong computational background to join our collaborative and interdisciplinary team. The successful candidate will join an exciting research project exploring how synonymous mutations, often assumed to be silent, shape gene expression and cellular phenotypes. Leveraging large-scale synonymous saturation mutagenesis libraries and a powerful suite of reporters already established in the lab, we systematically measure the impact of thousands of mutations across multiple molecular layers, from RNA and protein abundance and stability to protein function and cellular fitness. By integrating these complementary readouts, we aim to uncover the mechanisms through which synonymous sequence variation influences biological function. Your role will be to turn these rich, multidimensional datasets into biological insights. Working closely with experimental researchers, you will have the opportunity to develop and apply innovative computational approaches to identify patterns, uncover underlying mechanisms, and build predictive models linking genetic variation to molecular and cellular phenotypes. The project offers considerable freedom to explore new ideas and shape the computational direction of the research.

About the lab: The Laboratory for Genome Editing and System Genetics (Sibylle Vonesch Lab) at the VIB-KU Leuven Center for Microbiology develops high-throughput CRISPR technologies to understand how genetic variation shapes biological function. At the heart of its research is MAGESTIC, a CRISPR-based technology that enables precise engineering of thousands of defined mutations in parallel, linking each mutation to a unique DNA barcode to track variants and quantify their functional effects through high-throughput screens.

Key Responsibilities

• Turn rich, multidimensional mutagenesis datasets into biological insights
• Develop and apply innovative computational approaches to identify patterns and uncover underlying mechanisms
• Build predictive models linking genetic variation to molecular and cellular phenotypes
• Work closely with experimental researchers and help shape the computational direction of the project

Qualifications

• PhD in computational biology, bioinformatics, systems biology, genomics, computer science, or a related quantitative field.

Experience

Demonstrated experience in analyzing and integrating complex large-scale datasets and extracting biological insights. Experience with advanced computational and statistical approaches, ideally including machine learning and/or artificial intelligence. Prior experience with high-throughput mutagenesis data is an advantage but not a must.

Skills Required

• Large-scale data analysis and integration
• Advanced computational and statistical methods
• Machine learning and/or artificial intelligence (ideal)
• Excellent spoken and written English
• Collaborative, self-motivated team player

Additional Information

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