Job Listings

Postdoctoral Fellow

Company:
Harvard University
Job Location:
Cambridge, 02138
Category:
Biology
Type:
Full-Time

School: Faculty of Arts and Sciences

Department/Area: Stem Cell and Regenerative Biology

Position Description

The Arlotta lab at HSCRB is working to identify the cellular and molecular pathways disrupted in brain disorders such as schizophrenia and autism, by utilizing recent advances in genetics and genomics. We are developing and applying tools to understand how implicated genes act in neurons and circuits. We use large scale, unbiased, systematic approaches in collaborative multidisciplinary research teams.

This postdoctoral fellowship in the Arlotta lab involves computational analysis of functional genomics datasets with an emphasis on single-cell omics. This person will develop new computational methods to analyze novel RNAseq, ATACseq, and Spatial Transcriptomics datasets as well as optimally leveraging integration with existing 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.  This researcher will work in close collaboration with laboratory scientists on a range of projects with a specific emphasis on the interpretation of single cell results within a biological context. The position also entails close collaborations with multiple groups, many at the Stanley Center in the Broad Institute.

CHARACTERISTIC DUTIES
  •       Analyze large scRNA-Seq datasets to profile transcriptomes from brain organoid models and neuronal cell populations, 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.
  •       Explore advancing technologies such as spatial transcriptomoics, combined single-cell ATAC and RNA multiomics, and pooled CRISPR screening.     
  •       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 computational biologists experienced with various sequencing technologies, including those at the Stanley Center, to learn, discuss, and integrate the most appropriate solution for an experiment or project.
ABOUT THE LAB

We encourage applicants to read more about our scientific goals on our lab website. In addition, the Arlotta lab is actively working to become an equitable and inclusive community. The lab recognizes the inequity and bias within the academic community and is dedicated to our continuous self-education and activism in these areas. Future members are strongly encouraged to be active participants in these endeavors as part of their scientific career.

Basic Qualifications

  •       Ph.D. degree in Bioinformatics, Biology, Computer Science, or other relevant scientific discipline or equivalent experience is required.

Additional Qualifications

  •       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
  •       Basic understanding of molecular biology and next generation sequencing is highly preferred
  •       Familiarity with single cell data analysis is preferred, but not required.     
  •       Experience using a shared computing environment and a scheduler such as SLURM or qsub preferred     
  •       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

Contact Information

Zachary Trayes-Gibson

Contact Email: zachary_trayes-gibson@harvard.edu

Equal Opportunity Employer

We are an equal opportunity employer and all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability status, protected veteran status, gender identity, sexual orientation, pregnancy and pregnancy-related conditions or any other characteristic protected by law.

Minimum Number of References Required: 3

Maximum Number of References Allowed: 5

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