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 successWe’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.