... and spatial transcriptomics. • Knowledge of FDA, GxP, and pharmaceutical research quality ... computational pathology or digital pathology. Company : When progress demands experience, that ...
... and spatial transcriptomics. • Knowledge of FDA, GxP, and pharmaceutical research quality ... computational pathology or digital pathology. Company : When progress demands experience, that ...
... spatial transcriptomics, and ATAC-seq data. * Build, execute, and maintain reproducible computational workflows for quality control, read alignment, differential expression analysis, and data ...
... spatial transcriptomics, and ATAC-seq data. * Build, execute, and maintain reproducible computational workflows for quality control, read alignment, differential expression analysis, and data ...
... spatial transcriptomics, and ATAC-seq data. * Build, execute, and maintain reproducible computational workflows for quality control, read alignment, differential expression analysis, and data ...
... spatial transcriptomics, and ATAC-seq data. * Build, execute, and maintain reproducible computational workflows for quality control, read alignment, differential expression analysis, and data ...
Post Doctoral.Post Doctoral.Associate
$47K - $64K/yr
Multi-omic profiling (scRNA-seq, ATAC-seq, spatial transcriptomics) with integrative analysis The ... Ongoing collaborations with engineering and computational groups provide opportunities to ...
Post Doctoral.Post Doctoral.Associate
$47K - $64K/yr
Multi-omic profiling (scRNA-seq, ATAC-seq, spatial transcriptomics) with integrative analysis The ... Ongoing collaborations with engineering and computational groups provide opportunities to ...
... spatial transcriptomics, proteomics, cfDNA) using advanced modeling techniques. * Experience with computational pathology and AI-driven image analysis in the context of diagnostic innovation.
... spatial transcriptomics, proteomics, cfDNA) using advanced modeling techniques. * Experience with computational pathology and AI-driven image analysis in the context of diagnostic innovation.
... spatial transcriptomics, etc). * Demonstrated experience with statistical hypothesis testing ... Fluency with experimental and/or computational methods for generating and interrogating high ...
... spatial transcriptomics, etc). * Demonstrated experience with statistical hypothesis testing ... Fluency with experimental and/or computational methods for generating and interrogating high ...
Candidates must be proficient in advanced statistical and computational methods and have a solid ... Familiarity with spatial transcriptomics is a plus. The successful applicant will have an ...
Candidates must be proficient in advanced statistical and computational methods and have a solid ... Familiarity with spatial transcriptomics is a plus. The successful applicant will have an ...
Translational Post Doctoral Researcher - Agentic AI for Neurodegeneration
Spring House, PA · On-site +1
$110K/yr
Post Doc - Data Analytics & Computational Sciences Job Category: Career Program All Job Posting ... spatial resolution, temporal scale, and dimensionality - into unified analytical frameworks
Translational Post Doctoral Researcher - Agentic AI for Neurodegeneration
Spring House, PA · On-site +1
$110K/yr
Post Doc - Data Analytics & Computational Sciences Job Category: Career Program All Job Posting ... spatial resolution, temporal scale, and dimensionality - into unified analytical frameworks
Translational Post Doctoral Researcher - Agentic AI for Neurodegeneration
Spring House, PA · On-site +1
$110K/yr
Post Doc - Data Analytics & Computational Sciences Job Category: Career Program All Job Posting ... spatial resolution, temporal scale, and dimensionality - into unified analytical frameworks
Translational Post Doctoral Researcher - Agentic AI for Neurodegeneration
Spring House, PA · On-site +1
$110K/yr
Post Doc - Data Analytics & Computational Sciences Job Category: Career Program All Job Posting ... spatial resolution, temporal scale, and dimensionality - into unified analytical frameworks
Computational Spatial Transcriptomics information
What are some typical challenges faced when working in computational spatial transcriptomics, and how can new team members prepare for them?
Professionals in computational spatial transcriptomics often encounter challenges related to handling and analyzing large, complex datasets that combine spatial and gene expression information. Integrating data from different technologies and ensuring data quality can be demanding, requiring strong programming skills and familiarity with bioinformatics pipelines. New team members can prepare by strengthening their skills in statistical analysis, programming languages like Python or R, and staying updated on the latest spatial transcriptomics techniques. Collaborating closely with experimental biologists and data scientists is also key to overcoming these challenges and driving successful research outcomes.
What is the difference between Computational Spatial Transcriptomics vs Computational Biologist?
| Aspect | Computational Spatial Transcriptomics | Computational Biologist |
|---|---|---|
| Required Credentials | Advanced degrees in bioinformatics, computational biology, or related fields; experience with spatial data analysis | Typically a PhD or Master's in biology, bioinformatics, or related disciplines; strong programming skills |
| Work Environment | Research labs, biotech companies, academic institutions focusing on spatial genomics | Research institutions, biotech firms, academia working on biological data analysis |
| Industry Usage | Specialized in spatial transcriptomics techniques and data interpretation | Broad biological data analysis across various fields |
Computational Spatial Transcriptomics focuses on analyzing spatial gene expression data within tissues, requiring specialized skills in spatial data processing. In contrast, Computational Biologists work on a wider range of biological data types. While both roles involve bioinformatics expertise, the former emphasizes spatial data analysis techniques specific to transcriptomics.
What is computational spatial transcriptomics?
Computational spatial transcriptomics is a field that combines advanced computational methods with spatial transcriptomics, a technique that measures gene expression within the physical context of tissue samples. It involves processing and analyzing large datasets to map where specific genes are active within tissues, helping researchers understand how cells interact and function in their native environments. This approach is crucial for studies in developmental biology, cancer research, and neuroscience, as it provides insights into cellular organization and tissue architecture. Computational tools help extract meaningful patterns from complex data, enabling discoveries that were previously impossible with traditional methods.
What are the key skills and qualifications needed to thrive as a Computational Spatial Transcriptomics Scientist, and why are they important?
To excel in Computational Spatial Transcriptomics, you need a strong background in bioinformatics, genomics, and statistical data analysis, typically supported by advanced degrees in computational biology or related fields. Familiarity with programming languages (such as R and Python), spatial transcriptomics platforms (like 10x Genomics Visium), and high-throughput sequencing data analysis tools is essential. Strong problem-solving skills, attention to detail, and effective communication are crucial soft skills for interpreting complex datasets and collaborating with multidisciplinary teams. These competencies ensure accurate data interpretation, innovative research, and successful integration of spatial transcriptomics insights into biological and clinical applications.
What are popular job titles related to Computational Spatial Transcriptomics jobs in Pennsylvania? For Computational Spatial Transcriptomics jobs in Pennsylvania, the most frequently searched job titles are:
What job categories do people searching Computational Spatial Transcriptomics jobs in Pennsylvania look for? The top searched job categories for Computational Spatial Transcriptomics jobs in Pennsylvania are:
- Computational Biology Faculty
- Postdoctoral Virology
- Postdoctoral Chemistry
- Postdoctoral Position Parasitology Immunology
- Single Cell Omics
- Postdoctoral In Reproductive Biology
- Volunteer Bioinformatics Computational Biology
- Volunteer Remote Computational Chemistry
- Organic Chemistry Postdoctoral
- Spatial Transcriptomics Omics
Principal Scientist - Computational Histopathology Lead
Collegeville, PA • On-site
Full-time
Posted 28 days ago
Job description
Job Summary:
Yoh, A Day & Zimmermann Company is seeking a Principal Scientist – Computational Histopathology Lead for a contract opportunity in Collegeville, PA. This role will serve as the scientific and technical lead for computational pathology initiatives and is responsible for leading the design, development, validation, and deployment of advanced image analysis and machine learning solutions.
Responsibilities:
• Provide scientific leadership and technical oversight for computational pathology programs supporting biomarker discovery and translational research initiatives.
• Define and execute computational pathology strategies aligned with project objectives and scientific priorities.
• Lead multidisciplinary teams consisting of computational scientists, pathologists, data engineers, image analysts, and laboratory personnel.
• Serve as the primary scientific liaison between research, pathology, bioinformatics, translational medicine, and external stakeholders.
• Present scientific findings, project updates, and strategic recommendations to senior leadership and project governance committees.
• Lead the development, validation, optimization, and deployment of computational pathology pipelines for histological image analysis.
• Design and implement machine learning and artificial intelligence approaches for tissue classification, segmentation, biomarker quantification, and predictive modeling.
• Oversee development of image analysis algorithms using commercial and open-source platforms, including Halo AI, Visiopharm, QuPath, and CellProfiler.
• Ensure analytical methodologies are scientifically rigorous, reproducible, and fit for purpose.
• Establish best practices for algorithm validation, quality control, and performance monitoring.
• Collaborate with pathology and translational research teams to identify and validate tissue-based biomarkers.
• Support development of quantitative image analysis methodologies to assess biomarker expression and spatial biology features.
• Interpret histopathological and computational findings to generate actionable scientific insights.
• Contribute to translational research strategies that support preclinical and clinical development programs.
• Lead development of metadata ingestion, reconciliation, and data integration strategies supporting digital pathology ecosystems.
• Oversee implementation of automated workflows that integrate image data, metadata, and analytical outputs into enterprise image management systems.
• Ensure data integrity, traceability, and compliance with organizational data standards.
• Collaborate with data engineering and informatics teams to improve data accessibility and analytical efficiency.
• Establish project governance frameworks, including stage gates, milestone reviews, risk management processes, and performance metrics.
• Ensure all computational methodologies and deliverables are documented according to pharmaceutical industry standards and Good Documentation Practices (GDP).
• Review and approve technical reports, validation documentation, protocols, and scientific deliverables.
• Identify project risks and develop mitigation strategies to ensure successful execution.
• Support audits, inspections, and regulatory inquiries related to computational pathology activities.
• Partner with pathologists, translational scientists, laboratory teams, bioinformaticians, statisticians, and external vendors.
• Facilitate regular project review meetings and governance forums.
• Communicate complex technical concepts to both scientific and non-technical stakeholders.
• Drive alignment across cross-functional teams to ensure successful project execution.
Qualifications:
Required:
• Master's degree or PhD in Computational Biology, Bioinformatics, Biomedical Engineering, Computer Science, Data Science, Pathology, Biostatistics, or related scientific discipline.
• 10+ years of experience in computational pathology, digital pathology, image analysis, biomarker research, translational medicine, or related disciplines.
• Minimum 5 years of leadership experience managing multidisciplinary scientific teams and complex research programs.
• Demonstrated experience supporting pharmaceutical, biotechnology, CRO, or translational research organizations.
• Experience leading computational pathology initiatives from concept through validation and deployment.
• Experience managing external vendors, collaborators, and scientific partnerships.
• Experience with multiplex imaging technologies, immunohistochemistry (IHC), immunofluorescence (IF), and spatial transcriptomics.
• Knowledge of FDA, GxP, and pharmaceutical research quality standards.
• Experience working with automated staining platforms such as Ventana Discovery Ultra and Leica Bond RX.
• Publications, patents, or recognized scientific contributions in computational pathology or digital pathology.
Company:
When progress demands experience, that’s where we come in. Founded in 1940, the company is headquartered in Kings Mountain, USA, with a team of 1001-5000 employees. The company is currently Late Stage.
Yoh, A Day & Zimmermann Company is seeking a Principal Scientist – Computational Histopathology Lead for a contract opportunity in Collegeville, PA. This role will serve as the scientific and technical lead for computational pathology initiatives and is responsible for leading the design, development, validation, and deployment of advanced image analysis and machine learning solutions.
Responsibilities:
• Provide scientific leadership and technical oversight for computational pathology programs supporting biomarker discovery and translational research initiatives.
• Define and execute computational pathology strategies aligned with project objectives and scientific priorities.
• Lead multidisciplinary teams consisting of computational scientists, pathologists, data engineers, image analysts, and laboratory personnel.
• Serve as the primary scientific liaison between research, pathology, bioinformatics, translational medicine, and external stakeholders.
• Present scientific findings, project updates, and strategic recommendations to senior leadership and project governance committees.
• Lead the development, validation, optimization, and deployment of computational pathology pipelines for histological image analysis.
• Design and implement machine learning and artificial intelligence approaches for tissue classification, segmentation, biomarker quantification, and predictive modeling.
• Oversee development of image analysis algorithms using commercial and open-source platforms, including Halo AI, Visiopharm, QuPath, and CellProfiler.
• Ensure analytical methodologies are scientifically rigorous, reproducible, and fit for purpose.
• Establish best practices for algorithm validation, quality control, and performance monitoring.
• Collaborate with pathology and translational research teams to identify and validate tissue-based biomarkers.
• Support development of quantitative image analysis methodologies to assess biomarker expression and spatial biology features.
• Interpret histopathological and computational findings to generate actionable scientific insights.
• Contribute to translational research strategies that support preclinical and clinical development programs.
• Lead development of metadata ingestion, reconciliation, and data integration strategies supporting digital pathology ecosystems.
• Oversee implementation of automated workflows that integrate image data, metadata, and analytical outputs into enterprise image management systems.
• Ensure data integrity, traceability, and compliance with organizational data standards.
• Collaborate with data engineering and informatics teams to improve data accessibility and analytical efficiency.
• Establish project governance frameworks, including stage gates, milestone reviews, risk management processes, and performance metrics.
• Ensure all computational methodologies and deliverables are documented according to pharmaceutical industry standards and Good Documentation Practices (GDP).
• Review and approve technical reports, validation documentation, protocols, and scientific deliverables.
• Identify project risks and develop mitigation strategies to ensure successful execution.
• Support audits, inspections, and regulatory inquiries related to computational pathology activities.
• Partner with pathologists, translational scientists, laboratory teams, bioinformaticians, statisticians, and external vendors.
• Facilitate regular project review meetings and governance forums.
• Communicate complex technical concepts to both scientific and non-technical stakeholders.
• Drive alignment across cross-functional teams to ensure successful project execution.
Qualifications:
Required:
• Master's degree or PhD in Computational Biology, Bioinformatics, Biomedical Engineering, Computer Science, Data Science, Pathology, Biostatistics, or related scientific discipline.
• 10+ years of experience in computational pathology, digital pathology, image analysis, biomarker research, translational medicine, or related disciplines.
• Minimum 5 years of leadership experience managing multidisciplinary scientific teams and complex research programs.
• Demonstrated experience supporting pharmaceutical, biotechnology, CRO, or translational research organizations.
• Experience leading computational pathology initiatives from concept through validation and deployment.
• Experience managing external vendors, collaborators, and scientific partnerships.
• Experience with multiplex imaging technologies, immunohistochemistry (IHC), immunofluorescence (IF), and spatial transcriptomics.
• Knowledge of FDA, GxP, and pharmaceutical research quality standards.
• Experience working with automated staining platforms such as Ventana Discovery Ultra and Leica Bond RX.
• Publications, patents, or recognized scientific contributions in computational pathology or digital pathology.
Company:
When progress demands experience, that’s where we come in. Founded in 1940, the company is headquartered in Kings Mountain, USA, with a team of 1001-5000 employees. The company is currently Late Stage.
About Yoh, A Day & Zimmermann
Sourced by ZipRecruiter
Industry
Recruiting and staffing services
Company size
5,001 - 10,000 Employees
Headquarters location
Philadelphia, PA, US
Year founded
1940