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Postdoc Single Cell Rna Sequencing Analysis Jobs in Maryland

Axle

Bioinformatics Engineer

Rockville, MD · On-site

... RNA-seq analysis (both bulk and single-cell) and DNA sequencing workflows (variant calling, methylation) • Working knowledge of Google Cloud Platform services (Compute Engine, Cloud Storage, Batch ...

Axle

Bioinformatics/Data Scientist

Frederick, MD · On-site

The successful candidate will analyze complex datasets including single-cell RNA sequencing, bulk RNA sequencing, proteomics, and metabolomics data from various organoid systems and their tissue ...

Axle

Bioinformatics/Data Scientist

Frederick, MD · On-site

The successful candidate will analyze complex datasets including single-cell RNA sequencing, bulk RNA sequencing, proteomics, and metabolomics data from various organoid systems and their tissue ...

Axle

Bioinformatics/Data Scientist

Frederick, MD

The successful candidate will analyze complex datasets including single-cell RNA sequencing, bulk RNA sequencing, proteomics, and metabolomics data from various organoid systems and their tissue ...

Axle

Bioinformatics Engineer

Rockville, MD · On-site

Strong experience with RNA-seq analysis (both bulk and single-cell) and DNA sequencing workflows (variant calling, methylation) * Working knowledge of Google Cloud Platform services (Compute Engine ...

Axle

Bioinformatics Engineer

Rockville, MD · On-site

Strong experience with RNA-seq analysis (both bulk and single-cell) and DNA sequencing workflows (variant calling, methylation) * Working knowledge of Google Cloud Platform services (Compute Engine ...

Axle

Bioinformatics Engineer

Rockville, MD · On-site

Strong experience with RNA-seq analysis (both bulk and single-cell) and DNA sequencing workflows (variant calling, methylation) * Working knowledge of Google Cloud Platform services (Compute Engine ...

Umd

Postdoctoral Associate

College Park, MD

$75K - $80K/yr

... single-cell context, though projects related to long-read RNA-sequencing and to foundational ... Experience with handling and analyzing large genomics datasets, as well as experience in developing ...

Axle

Data Scientist II

Rockville, MD · On-site

... exome sequencing (WGS/WES), metagenomics, metabolomics, and proteomics, as well as clinical ... labs in their analysis workflows including bulk RNA-seq (QC, DEG, GSEA), single-cell RNA-seq ...

Axle

Data Scientist II

Rockville, MD

... exome sequencing (WGS/WES), metagenomics, metabolomics, and proteomics, as well as clinical ... labs in their analysis workflows including bulk RNA-seq (QC, DEG, GSEA), single-cell RNA-seq ...

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Postdoc Single Cell Rna Sequencing Analysis information

What is the difference between Postdoc Single Cell Rna Sequencing Analysis vs Postdoc Bioinformatics?

AspectPostdoc Single Cell Rna Sequencing AnalysisPostdoc Bioinformatics
Required CredentialsPhD in Biology, Genetics, or related field; experience in sequencing data analysisPhD in Computer Science, Bioinformatics, or related field; programming skills essential
Work EnvironmentResearch labs focusing on genomics and cell biologyResearch institutions, biotech companies, or academic labs with computational focus
Employer & Industry UsageBiotech, academic research, pharmaceutical companiesBiotech, healthcare, academic research, industry R&D

Postdoc Single Cell Rna Sequencing Analysis specialists focus on analyzing single-cell transcriptomics data, often requiring biological expertise and lab experience. In contrast, Postdoc Bioinformatics roles emphasize computational skills and software development to interpret large datasets across various biological contexts. Both roles are vital in genomics research but differ in their primary focus and skill set.

What are the key skills and qualifications needed to thrive as a postdoc in single cell RNA sequencing analysis, and why are they important?

To thrive as a Postdoc in Single Cell RNA Sequencing Analysis, you need a strong background in molecular biology, genomics, and bioinformatics, typically supported by a PhD in a relevant field. Proficiency with computational tools such as R, Python, and specialized single-cell analysis platforms (e.g., Seurat, Scanpy), as well as experience with data visualization and next-generation sequencing, is essential. Strong problem-solving abilities, effective communication, and collaboration skills help distinguish top candidates in interdisciplinary research environments. These skills enable accurate data interpretation, drive innovation, and support impactful scientific discoveries in complex biological systems.

What are some common challenges faced by postdocs working in single cell RNA sequencing analysis, and how can they be addressed?

Postdocs in single cell RNA sequencing analysis often encounter challenges such as managing large and complex datasets, integrating multi-omic data, and staying current with rapidly evolving bioinformatics tools. Collaborating closely with wet lab scientists and computational biologists is essential to interpret results accurately and to troubleshoot technical issues. Building strong programming and statistical skills, as well as actively participating in lab meetings and seminars, can help address these challenges and contribute to both personal growth and successful project outcomes.

What does a postdoc in single cell RNA sequencing analysis do?

A Postdoc in Single Cell RNA Sequencing (scRNA-seq) Analysis specializes in analyzing gene expression data from individual cells. Their main responsibilities include processing raw sequencing data, performing quality control, identifying cell types or states, and interpreting biological insights from the data. They often develop or apply computational methods to handle large datasets, collaborate with experimental biologists, and present findings through publications or conferences. The ultimate goal is to understand cellular heterogeneity and uncover new biological mechanisms at the single-cell level.
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Postdoc Single Cell Rna Sequencing Analysis jobs near you

Bioinformatics/Data Scientist

Nextonic Solutions LLC

Frederick, MD

$100K - $185K/yr

Full-time

Posted 23 days ago

Job description

Nextonic Solutions is seeking a Bioinformatics/Data Scientist to join our vibrant team at the National Institutes of Health (NIH) supporting the Standardized Organoid Model Center in Frederick, MD. The Standardized Organoid Model Center is an NIH-funded initiative dedicated to advancing organoid research through the development of validated, reproducible, and well-characterized organoid models. The center brings together interdisciplinary teams of researchers to establish standardized protocols, develop quality control measures, and create resources that will benefit the broader organoid research community.


Overview


The Bioinformatics/Data Scientist will conduct comprehensive analyses of multi-omics data generated from organoid systems and corresponding normal tissues. This position is central to the SOM Center's research objectives, focusing on characterizing organoid fidelity, identifying biomarkers of successful differentiation, and developing computational frameworks for organoid quality assessment.


Responsibilities


  • The successful candidate will analyze complex datasets including single-cell RNA sequencing, bulk RNA sequencing, proteomics, and metabolomics data from various organoid systems and their tissue counterparts.
  • The position will develop and implement computational pipelines for data processing, quality control, and statistical analysis.
  • A major component of the role involves integrating SOM-generated data with publicly available datasets to benchmark organoid characteristics against normal tissue profiles.
  • The position requires close collaboration with experimental teams to interpret results and guide protocol optimization, as well as contributing to manuscript preparation and presenting findings at scientific conferences.


Qualifications

  • Candidates must hold a PhD in bioinformatics, computational biology, biostatistics, or a related quantitative field.
  • Extensive experience with single-cell data analysis, including familiarity with tools such as Seurat, Scanpy, or similar platforms, is essential.
  • Strong programming skills in R and Python are required, along with experience in statistical analysis and data visualization.
  • Knowledge of proteomics and metabolomics data analysis workflows is necessary.


Preferred Qualifications


  • Previous experience analyzing organoid datasets is strongly preferred.
  • Experience with machine learning approaches for biological data, familiarity with pathway analysis tools, and knowledge of developmental biology principles will be considered valuable assets.
  • Experience with high-performance computing environments and version control systems is desirable.