Postdoctoral Scientist (Computational Biology)

Job ID: 27770
Job date: 2017-12-07
End Date:

Company : Cedars-Sinai 

Country :

Role : Postdoc  | Research Scientist 


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Job Description:
The Knott Lab is searching for a computational biologist Postdoctoral Scientist that is interested in applying single cell profiling technologies to study breast cancer progression. We are part of the Center for Bioinformatics and Functional Genomics at Cedars Sinai Medical Institute, in Los Angeles California. The lab is currently focused on developing molecular and computational tools to study the mechanisms that cancer cells employ to metastasize and evade therapy. We are particularly interested in next-generation therapeutics that target the interactions between malignant and non-malignant tumor cell types, such as immune checkpoint inhibitors. The chosen candidate will work alongside oncologists and bench scientists to generate clinically actionable hypotheses from novel single cell datasets.

These will include 10X genomics data from dissociated tumors as well as information from in situ experiments, where single cell expression profiles are extracted from intact tumor tissue. In addition, the selected applicant will be a key contributor to the design and data analysis portion of follow-up validation studies, which employ our novel genome-wide CRISPR reagents. This position offers a unique opportunity for a post doc with a computational specialty to fully immerse themselves in the discovery process.

Essential job duties and responsibilities:

• May assist in preparation of grant proposals, but is not responsible for generating grant funds.

• May participate in publications and presentations as author or co-author.

• Designs and performs experiments. Will keep appropriate experimental records and documentation and analyze the results with the Principal Investigator.

• May develop, adapt, and implement new research techniques and protocols.

• Analyzes, interprets, summarizes and compiles data.

• Performs routine and complex laboratory procedures throughout training period.

• Operates and maintains equipment and instruments.

Physical demands: (e.g., lifting, standing, walking)

Able to perform moderate lifting. Able to sit, stand, and walk for prolonged periods of time. Able to operate standard office equipment. Able to exercise physical ability and perspective acuity to satisfactorily perform essential job functions.

Qualification Requirements/Preferences:

Education Certifications/Licensure Experience Physical Abilities Education:

PhD required, Computational Biology or related field, preferred

Experience:

Works independently on research projects designed by a mentor (typically the PI) within area of specialization. Acquires thorough technical and theoretical knowledge of research project and objectives during one to five (1-5) year post-doctoral appointment. Demonstrated aptitude to perform experimental protocols and procedures, including detailed data collection, and analysis and operation and maintenance of specialized equipment. Knowledge of safety standards and maintenance of specialized equipment.

The candidate should be comfortable in an HPC or Cloud environment. They will have a graduate level understanding of statistics, probability and algorithms. They should be fully proficient in at least one scripting language (e.g. Python) and at least one mathematical package (e.g. Matlab or R). The ideal candidate will have a background working with single cell sequencing data and a general understanding of cancer biology.


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Additional Info:
Relevant Publications:

Erard N*, Knott SRV*, Hannon GJ. A CRISPR resource for individual, combinatorial, or multiplexed gene knockout. Mol Cell. 2017, 67(2):384-354. (*co-first author)

Wagenblast E, Soto M, Gutiérrez-Ángel S, Hartl CA, Gable AL, Maceli AR, Erard N, Williams AM, Kim SY, Dickopf S, Harrell CJ, Smith AD, Perou CM, Wilkinson JE, Hannon GJ & Knott SRV. A model of breast cancer heterogeneity reveals vascular mimicry as a driver of metastasis. Nature. 2015, 520:358-362.

Knott SRV*, Maceli A*, Erard N*, Chang K*, Marran K, Zhou X, Gordon A, El Demerdash O, Wagenblast E, Kim S, Fellmann C, Hannon GJ. A computational algorithm to predict shRNA potency. Mol Cell. 2014, 56(6):796-807. (*co-first author)

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