Sra 1

Job description

As an SRA I, you will play a vital role in Dr. Chou's research group, which focuses on evaluating the mechanisms of sensitivity and resistance to antibody drug conjugates (ADCs) and CAR T-cell therapies. The group has developed innovative experimental models and utilizes advanced sequencing techniques. Your primary responsibilities will involve validating research findings, conducting functional tests, and providing bioinformatics support.
• 40%: Assists with bioinformatics analyses of transcriptomic and whole-exome sequencing data.
• 35%: Assists with sgRNA cloning to validate prior genetic screens, including ordering oligonucleotides, transforming bacteria with sgRNA constructs, and confirming sequences through Sanger sequencing.
• 15%: Performs validation of transcriptomic data using quantitative PCR (qPCR).
• 10%: Performs other related laboratory duties and responsibilities as assigned.

Required Qualifications:
• BA/BS in Biology or related as well as in Computational Biology/Computer Science with biology applications
• Experience in R Studio and/or Python to analyze single-cell RNA sequencing, bulk RNA sequencing, and whole-exome sequencing data
• Experience in molecular biology techniques such as sgRNA cloning and sequencing, gel purification, miniprep, and expression of lentiviruses using HEK293T/LentiX cells
• Experience in lentiviral generation, transduction, and validation techniques such as qPCR and/or flow cytometry
• Experience with Incucyte to monitor cell growth
• Awareness of different types of clinical research studies
• Willingness and ability to learn new methods and skills
• Ability to work independently and as a member of a research team
• Ability to demonstrate interpersonal communication skills
• Ability to demonstrate organizational skills; ability to prioritize tasks, coordinate work tasks with others, and meet multiple deadlines
• Ability to perform simple mathematical calculations

Preferred Qualifications:
• Prior experience with working in a wet lab and with computational biology experience