The group of Dr. David Casero applies Bioinformatics, Computational Biology and high-throughput sequencing data to analyze gene regulatory pathways in stem cell and immunobiology. We are particularly interested on understanding the establishment and maintenance of normal tissue homeostasis, supported by molecular interactions between members of the tissue microenvironment (e.g., epithelial, mesenchymal and immune cells) and environmental factors (e.g., signals modulated by the microbiome). With the help of our collaborators, our lab aims to harness the power of multi-omics libraries to generate novel data-driven hypothesis that can pave the way into future mechanistic and translational studies. For more information about Dr. Casero’s research please visit the group’s website at www.cedars-sinai.org/research/labs/casero-lab.html
A Research Bioinformatician position is open immediately to join our lab. The successful candidate will work within a collaborative team that aims to integrate phenotypic multi-omics data (transcriptomics, proteomics, metabolomics) into predictive and classification models. In this role, the candidate will develop a wide range of computational analysis pipelines, from experimental design to data analyses and visualization to inform drug-discovery efforts and follow-up functional experiments. Therefore, successful candidates should be proficient in the analysis of several multi-omics data types (bulk and single cell RNA-seq, ChIP-Seq, ATAC-seq, metagenomics, metatranscriptomics, spatial transcriptomics) and the use of bioinformatics software and databases.
Education:
Master of Science Degree in Computer Science, Electric Engineering, Computational biology, or Bioinformatics, or Master of Science/ Engineering in relevant fields (e.g. Biology with strong quantitative training, biostatistics with concentration in bioinformatics).
Experience and Skills:
3 years in research environment. Background and work knowledge in algorithms, scientific computing, and machine learning or statistics,
Familiar with C/C++, Java, Perl, python, and the Unix (Linux) environment.
Experiences in manipulating, analyzing, and annotating very large genomic (e.g. NGS) data sets both in exploratory and pipelined fashions.
Ability to apply knowledge of information software and/or hardware to provide solutions and/or support.
Ability to apply advanced knowledge of science/learning/specialized intellectual instruction to analyze, interpret or make deductions from varying facts or circumstances.
Record of significant contribution to peer-reviewed publications.
Excellent communication and presentation skills.