A little about us:
The Lieber Institute for Brain Development (LIBD) was established in 2010 to plot a new course in biomedical research that would change the lives of individuals affected with developmental brain disorders. We are one of the only research institutions in the world focused specifically on understanding how genes and the environment influence the way our brains develop that lead to conditions such as schizophrenia, autism, bipolar disorder and related developmental brain disorders. Through our cutting-edge research, answers are emerging and being translated into a robust pipeline of new drugs in development. And, by focusing on genes and their dynamic interplay with the environment, we are getting even closer to the “holy grail” in medical research, the discovery of strategies for primary prevention.
We are a group of dedicated, multidisciplinary and optimistic researchers, working at the cutting edge of science, utilizing state-of-the-art tools to unlock the mystery of the brain and transform the way we approach the development of new treatments, and ultimately cures. LIBD is an independent 501(c)(3) medical research institute located in the Bioscience Park on the campus of the Johns Hopkins School of Medicine and Hospital in Baltimore, MD.
has a Research Associate position in genomics data science available now. Our work is focused on understanding the roots and signatures of disease (particularly psychiatric disorders) by zooming in across dimensions of gene activity. We achieve this by studying gene expression at all expression feature levels (genes, exons, exon-exon junctions, and un-annotated regions) and by using different gene expression measurement technologies (bulk RNA-seq, single cell/nuclei RNA-seq, and spatial transcriptomics) that provide finer biological resolution and localization of gene expression. We work closely with collaborators from LIBD as well as from Johns Hopkins University (JHU) which reflects the cross-disciplinary approach and diversity in expertise needed to further advance our understanding of high throughout biology.
The Research Associate will work on single cell and spatial transcriptomics projects in close collaboration with the Molecular Neuroanatomy team under the direction of Dr. Kristen Maynard. These projects involve integration of spatial transcriptomics data from the 10x Genomics Visium platform with single cell multi-omic (RNA-seq and ATAC-seq) data from brain regions implicated in reward processing. This position will also be involved in new projects and technologies that will help us further our understanding of psychiatric disorders and addiction. We are looking for someone who can contribute to our team environment and who is very motivated to learn with us how to approach genomics data science applied to the human brain. Initiative to learn new methods, develop tools/resources for the scientific community, and perform work efficiently and carefully are essential. Interest in neuroscience and comfortability working and communicating with biologists is required.
Specific Duties and Responsibilities:
· Perform creative, rigorous, and reproducible analyses on multi-omics datasets generated from the human postmortem brain samples at LIBD from technologies such as bulk RNA-seq, single cell/nuclei RNA-seq, single cell ATAC-seq, and spatial transcriptomics.
· Utilize publicly available data to enrich our findings from the LIBD datasets.
· Develop open-source computational biology software such as R/Bioconductor packages with helper tools such as biocthis.
· Communicate with individuals with different expertise such as biologists, computational scientists, and biostatisticians.
· Present research results at team meetings, LIBD meetings, and scientific conferences as well as contribute to peer-reviewed publications and grant applications.
· Perform computational work on a high-performance computing environment called JHPCE (with helper tools like sgejobs), version control code with GitHub, and communicate with team members using Slack.
· Participate in continuous learning activities such as the LIBD rstats club and bootcamps.
Other duties as assigned.
· Bachelor’s degree in computer science, computational biology, biostatistics, bioinformatics, biomedical engineering, or a related field.
· A minimum of three years related experience is required
· Master’s degree preferred
Special Knowledge, Skills, and Abilities:
· Experience with genomic data analysis using R/Bioconductor packages for one or more projects
· Evidence of computational skills and code version control through GitHub or similar options
· Experience using Linux and high-performance computing environments
· Understanding of statistical methods such as linear regression, interaction models, ANOVA, t-Student and F distributions, p-values and false discovery rate (FDR)
· Strong organizational skills, attention to detail, excellent problem-solving skills, excellent written and verbal communication skills
· Desire to keep learning more about biology and computational methods, and ways to integrate them
· Experience performing computational analysis projects most commonly exemplified with peer-reviewed publications or pre-prints
· Understanding of how RNA-seq data is generated and familiarity with bioinformatics methods commonly used in the field
· R/Bioconductor package development experience
· Advanced statistics knowledge including exponential family distributions and mixed effect models
· Python experience is a plus as we do have potential python-based projects with image analysis
· Public presentation skills are a plus, and can be developed otherwise
** If accommodations are needed due to pregnancy or a disability, please contact firstname.lastname@example.org
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For additional information, applicants should visit https://covidinfo.jhu.edu/ https://covidinfo.jhu.edu/health-safety/covid-vaccination-information/
EEOC Statement: At the Lieber Institute, we are committed to a work environment of mutual respect where employment decisions are based on merit. As an equal opportunity employer, the Lieber Institute does not discriminate in employment opportunities on the basis of race, color, religion, color, sex, gender identity/expression, sexual orientation, pregnancy, marital status, age, national origin or ancestry, citizenship, disability (physical or mental), genetic information, military service, or other non-merit based factors protected by state or federal law or local ordinance, with regard to any position or employment for which the applicant or employee is qualified.