Scientist, Computational Biology

Colossal Biosciences•Dallas, TX

10h•Onsite

About The Position

An Affiliate of Colossal is seeking a talented computational biologist with strong analytical skills to tackle challenging genotype-to-phenotype questions. The successful candidate will collaborate with scientists and engineers to design and perform bioinformatics analyses and integrate genomic, epigenomic, transcriptomic, and proteomic datasets to support de-extinction efforts. The candidate must have experience in bioinformatics, computational biology, statistics, or comparative genomics. Preference will be given to candidates with a PhD who have demonstrated experience leveraging and interpreting machine learning / artificial intelligence frameworks to link genotype and phenotype using diverse comparative or functional genomic data and information in data-sparse or non-model organisms. This position will be based on-site in our Dallas, TX headquarters. Relocation assistance is available.

Requirements

Two years of bioinformatics experience in the following areas: Applied Statistics or Machine Learning / Artificial Intelligence in Genomics, Comparative Genomics, Functional Genomics / Multi-Omics, or Molecular Evolution.
Demonstrated ability building and training ML/AI models linking genotype and phenotype (e.g., sequence-to-function models) and experience with the popular libraries like Pytorch, Tensorflow, or OpenCV.
Capable of leveraging and integrating knowledge across multiple levels of biological organization to validate the outputs of complex analyses.
Demonstrated 2 years of experience with scripting languages, including but not limited to: Python, R, Perl, Ruby, Java, and BASH.
Ability to write and run custom bioinformatics scripts using existing published tools and occasionally tools developed to summarize the results in a digestible manner and deliver the information using established reporting procedures.
Proficiency with handling large-scale genomic data in an HPC (SGE, SLURM, PBS) Linux and/or cloud environment (e.g. AWS, Google Cloud, Azure).
Experience in using GIT version control software and maintaining well-documented, reproducible notebooks and workflows.
Ability to design and maintain databases (MySQL, PostgreSQL, MongoDB) and connect with visual platforms to curate and share data with non-bioinformatics team members.

Nice To Haves

Developing or implementing AI/ML frameworks and systems biology networks (e.g., interpretable aka visible deep neural networks like GenNet) for genotype-to-phenotype and functional predictions
Executing rigorous analyses of diverse functional epigenomics approaches (e.g., RNA-seq and ATAC-seq) and integrating multi-omics datasets to aid in understanding of gene expression regulation
Performing evolutionary and statistical genomics analyses, including population genetics analysis (e.g., runs of homozygosity and association mapping), genome-wide scans for evolutionary signatures and selective sweeps, and comparative genomics analyses associating genotype and phenotype (e.g., PAML inference of molecular evolution and phylogenetic regression)
Constructing, interpreting, and utilizing pangenome graphs, whole genome alignments, and gene homology relationships
Calling germline and somatic sequence variants from high-coverage WGS, low-coverage WGS with imputation, and sequencing libraries from degraded or ancient DNA
Statistical planning and collaboration with laboratory scientists on designing well-powered experiments to generate useful multi-omics data sets
Understanding of precision gene editing technologies like CRISPR/Cas9 systems

Responsibilities

Build machine learning or artificial intelligence models using diverse, integrative datasets
Run comparative, functional, and statistical genomics analysis
Run data analysis with biological data
Develop new tools in R/Python for data analysis and visualization
Curate and document raw and intermediate data and analysis software
Prepare reports and presentations to communicate findings to wet-bench biologists and leadership