Our Computational Biology Team plays a key role in developing prediction and optimization tools for payload design and manufacturing for  gene and cell therapy. We analyze complex  multi-omic datasets, particulary single-cell RNA-seq and DNA –based assays , to support our research efforts to improve drug design in gene and cell therapy. The Computational Biologist will actively participate to the development of WLG’s In-silico tools, pipelines and models.

As a Computational Biologist, here’s how you will make an impact:

You will:

• Provide bioinformatics analyses and contribute to the development of innovative approaches for various research and customer projects in collaboration with the other technical teams

• Develop and implement algorithms and statistical methods for the integration, visualization, and interpretation of complex biological datasets, with a strong focus on multi-omic data

• Participate and actively collaborate within cross-functional and cross-thematic projects, working under the supervision and guidance of project managers

• Support the improvement, design and maintenance of in-house data analysis pipelines and contribute to the integration of new computational methodologies and best practices

• Assist in the validation of machine learning solutions for vector and synthetic promoter designs, help advance WhiteLab Genomics’ internal R&D platforms

• Contribute to hypothesis generation and experimental design in computational biology

• Support internal research projects to advance core scientific methodologies, focusing on the development of methods leveraging multi-omic data to optimize the design of DNA sequences used in cell and gene therapies

• Present your findings and discoveries with the WhiteLab Genomics teams, enhancing our collective knowledge and contributing to our overall success

We’re eager to meet you if you

• Hold a recent PhD or Master’s degree in bioinformatics, computational biology, biostatistics, or a related field

• Have 1–4 years of experience in biological data analysis

• Possess strong programming skills in Python or R, with familiarity using bioinformatics toolkits and libraries (e.g. Bioconductor, scikit-learn, Scanpy, Seurat)

• Are proficient in the analysis of multi-omic datasets, such as single-cell RNA-seq, ATAC-seq, and other NGS data types

• Have a solid foundation in molecular biology and experience working with biological databases and reference resources (e.g., Ensembl, UniProt, NCBI).

• Demonstrate a strong understanding of statistical methods for analyzing biological data

• Can communicate complex scientific concepts clearly with internal and external stakeholders and work effectively in a collaborative research environment.

Nice to have

• Proficient in workflow management systems (e.g., Nextflow, Snakemake, Terra, Airflow) and containerization technologies (e.g., Docker, Singularity).

• Experience/knowledge in gene and cell therapy

• Experience in machine learning, deep learning methods and tools (e.g. TensorFlow, Keras)

• Experience working on cloud or HPC environments.

• Teams with Sara & Dina Z (comp-bio team)

• Teams with Dina L (People & Culture team)

• Interview in person (comp-bio team + P&C)