We are seeking a motivated Computational Scientist I to join Sheng Lab the at the Broad Institute of MIT and Harvard. Aligned with the mission of Stanley Center, we are a team of scientists studying the genetic basis and molecular mechanisms underlying schizophrenia and other brain disorders, anticipating that our research would be potentially useful for identifying more effective biomarkers and treatments.
Our group seeks to generate large datasets from genetic animal models in which schizophrenia risk genes are mutated and identify convergent and divergent patterns to better understand the mechanisms and circuits disrupted in mental illness. This candidate will collaborate with biologists and geneticists to analyze data from numerous modalities, including both transcriptomics and proteomics, with an aim towards identifying these pathways and enabling biological insight.
This position in the Stanley Center for Psychiatric Research involves computational analysis of functional genomics data, with a focus on bulk and single-nucleus RNA-Seq data. This scientist will apply existing computational methods, help develop new computational methods where needed, and interpret results within a biological context. This researcher will work in close collaboration with laboratory scientists on analysis of a range of projects with a strong emphasis on bulk, single-cell, and spatial RNA-seq approaches. The scope will include integration with other internal and external genomics datasets. The role will often involve rapid prototyping in support of a dynamic, fast-moving experimental program; it is focused on molecular biology applications relevant to investigation of brain function and psychiatric illness. The position will provide an opportunity to become a part of the computational biology community at the Broad Institute.
- Analyze large RNA-Seq datasets to profile transcriptomes from mouse brain, and interpret results to derive important biological insights into these models.
- Work with wet-lab biologists to design and implement appropriate experiments for future computational analysis.
- Provide results in formats accessible to a research community of biologists and geneticists.
- Develop, apply, document, and maintain computational tools, both for own use and to support analysis by biologist colleagues without formal computational training.
- Follow relevant scientific literature to ensure use of optimal methods and understand emerging practices across the field.
- Contribute to reports and papers for presentation and publication and present at scientific conferences, as appropriate.
- Regularly attend and present results at team meetings to share results, plan projects and experiments.
- Work with other Broad computational scientists experienced with RNA-Seq, including those in the Levin group, to learn, discuss, and integrate the most appropriate solution for an experiment or project.
- Ph.D. degree in Bioinformatics, Computer Science, or other relevant scientific discipline or equivalent experience is required.
- Should have a demonstrated proficiency in R and/or Python, or related languages
- Experience with and solid understanding of statistical analysis is required
- Familiarity with next-generation sequence data analysis tools, particularly those for RNA-seq
- Understanding of molecular biology and next generation sequencing is highly preferred
- Familiarity with single cell data analysis is preferred, but not required.
- Familiarity with working on a computing cluster/server (important skills: Unix/Linux, shell scripting)
- Experience designing computational methods and tools, including prior experience with algorithms relevant to computational biology, is a plus.
- Ability to work independently as well as part of an interdisciplinary team in a fast-paced environment, while making necessary connections with experts in various computational analysis groups
- Self-starter, highly motivated
- Excellent communication and interpersonal skills
- Excellent organization and time management skills
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