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Spatial Transcriptomics Jobs in California (NOW HIRING)

Arc Institute

Scientist, Bioinformatics

Palo Alto, CA · On-site

... omics, Perturb-seq, spatial transcriptomics), public and generated in-house. • Process sequencing data as they come off instruments, monitoring runs, producing actionable results, and ...

Acelabio, Inc

Business Development Director

San Diego, CA · On-site

$86K - $143K/yr

We deliver anatomic histopathology, immunostaining, digital image analysis, spatial transcriptomics, and biomarker endpoints to pharmaceutical and biotechnology sponsors. Our team is scientifically ...

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Spatial Transcriptomics information

What is spatial transcriptomics?

Spatial transcriptomics is an advanced technique that allows scientists to measure gene expression within the spatial context of tissue samples. Unlike traditional RNA sequencing, which loses information about where each gene is expressed, spatial transcriptomics preserves the physical location of gene activity in tissues. This helps researchers better understand how cells function within their native environments and interact with neighboring cells, which is especially valuable in fields like cancer research, neuroscience, and developmental biology. The method combines microscopy, molecular biology, and computational analysis to produce detailed maps of gene expression.

What are some common challenges faced by professionals working in spatial transcriptomics, and how can they be addressed?

Professionals in spatial transcriptomics often encounter challenges related to handling large, complex datasets and integrating spatial information with gene expression data. Ensuring high-quality sample preparation and mastering advanced imaging or sequencing technologies are also frequent hurdles. These challenges can be addressed by collaborating closely with multidisciplinary teams—including bioinformaticians, molecular biologists, and imaging specialists—and staying up-to-date with the latest software tools and protocols. Continuous learning and effective communication within the team are key to overcoming technical and analytical obstacles in this rapidly evolving field.

What are the key skills and qualifications needed to thrive as a Spatial Transcriptomics Scientist, and why are they important?

To thrive as a Spatial Transcriptomics Scientist, you need a strong background in molecular biology, genomics, and bioinformatics, typically supported by an advanced degree in a life science field. Familiarity with spatial transcriptomics platforms (such as 10x Genomics Visium), next-generation sequencing (NGS) technologies, and data analysis tools like R or Python is essential. Strong problem-solving skills, attention to detail, and effective communication are important soft skills for collaborating on interdisciplinary research projects. These skills and qualities are crucial for generating high-quality spatial gene expression data and translating findings into meaningful biological insights.
What are popular job titles related to Spatial Transcriptomics jobs in California? For Spatial Transcriptomics jobs in California, the most frequently searched job titles are:
What job categories do people searching Spatial Transcriptomics jobs in California look for? The top searched job categories for Spatial Transcriptomics jobs in California are:
What cities in California are hiring for Spatial Transcriptomics jobs? Cities in California with the most Spatial Transcriptomics job openings:
Spatial Transcriptomics jobs near you
BIOINFORMATICS PROGR

BIOINFORMATICS PROGR

University of California San Francisco

San Francisco, CA • On-site

Other

Posted 29 days ago

Job description

The Cardiovascular Genetics Center at University of California, San Francisco is seeking an experienced bioinfomatician to faciliate several lines of research that relate broadly to inherited forms of cardiovascular disease, regulation of gene expression in health and disease, and personalized genetic medicine. The incumbent will work closely with prinicpal investigators within the Genetic Center to design and implement analysis pipelines in several broad areas of computational biology including analysis of experimentally generated datasets in basic research, and analysis of patient-derived genomic data from UCSF patients as well as publicly available data repositories. The goal will be to make fundamental biological discoveries that can ultimately be translated to understanding, prevent, and treat heritable cardiovascular diseases.
More specifically, the role involves developing and utilizing computational tools and systems to analyze and interpret biological or other research data. Utilizes and develops algorithms, computational techniques, and statistical methodologies. Helps in the design of new experiments. Implements end-user needs in database searching and integration. Maintains the computational infrastructure and tracks the flow of samples and information for large-scale studies. Provides web-based bioinformatics and access to public and proprietary databases.
 
The scope of work will include anayzing biological datasets generated experimentally including but not limited to bulk and single cell multiomics sequencing, epigenomics data including ChIP-Seq and CUT&RUN, and imaging data including spatial transcriptomics; overlapping experimental data with publicaly available genomic and expression datasets including UK Biobank, All of Us, Heart Cell Atlas, and similar datasets. There will also be analysis of genome sequencing data including whole exome- and whole genome sequencing to discern rare and common variants implicated in cardiovascular disease.The incumbent will also implement machine learning algorithms to analyze biological datasets.  There will be frequent collaboration with bench scientists and clinicians in a highly collegial and stimulating scientific environment.

Required Qualifications

  • Bachelor's degree in biological science, computational / programming, or related area and / or equivalent experience / training
  • A minimum of 3 years of relevant work experience in bioinformatics or computational biology
  • Thorough knowledge of bioinformatics programming design, modification and implementation in relevant languages such as Python, R, and/or equivalent
  • Understanding of relational databases, web interfaces, and operating systems
  • Strong project management skills
  • Thorough knowledge of modern biology and applicable field of research
  • Communication skills to work with both technical and non-technical personnel in multiple fields of expertise and at various levels in the organization
  • Ability to communicate technical information in a clear and concise manner
  • Ability to interface with management on a regular basis
  • Self motivated, work independently or as part of a team, able to learn quickly, meet deadlines and demonstrate problem solving skills
  • Thorough knowledge of web, application and data security concepts and methods
  • Familiarity with cloud-based computing and high-performance computing environments
  • Proven expertise, as reflected in publication record, in analyzing high-throughput sequencing datasets, including RNA-sequencing, ATAC-sequencing, and chromatin occupancy studies
  • Thorough In-depth knowledge of genomic databases, tools, and statistical frameworks

Preferred Qualifications

  • PhD in Bioinformatics or a closely related field

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