... spatial transcriptomics and multi-omics data • Create high-performance computing and scalable ... computational pathology workflows • Proficiency in Python and scientific computing libraries • ...
... spatial transcriptomics and multi-omics data • Create high-performance computing and scalable ... computational pathology workflows • Proficiency in Python and scientific computing libraries • ...
Spatial transcriptomics and multi-omics data integration * High-performance computing and scalable ... Hands-on experience with whole slide image analysis and computational pathology workflows
Spatial transcriptomics and multi-omics data integration * High-performance computing and scalable ... Hands-on experience with whole slide image analysis and computational pathology workflows
... spatial transcriptomics. We model malignant glioma using glioma patient derived organoids and ... The labs are also a dynamic environment in which bench work and computational/systems biology ...
... spatial transcriptomics. We model malignant glioma using glioma patient derived organoids and ... The labs are also a dynamic environment in which bench work and computational/systems biology ...
... spatial transcriptomics. We model malignant glioma using glioma patient derived organoids and ... The labs are also a dynamic environment in which bench work and computational/systems biology ...
... spatial transcriptomics. We model malignant glioma using glioma patient derived organoids and ... The labs are also a dynamic environment in which bench work and computational/systems biology ...
Genomics / transcriptomics (bulk, single-cell, spatial) * Epigenomics, proteomics, or multi-omics ... D in Computer Science, Bioinformatics, Computational Biology, Data Science, or a related field
Genomics / transcriptomics (bulk, single-cell, spatial) * Epigenomics, proteomics, or multi-omics ... D in Computer Science, Bioinformatics, Computational Biology, Data Science, or a related field
Genomics / transcriptomics (bulk, single-cell, spatial) * Epigenomics, proteomics, or multi-omics ... D in Computer Science, Bioinformatics, Computational Biology, Data Science, or a related field
Genomics / transcriptomics (bulk, single-cell, spatial) * Epigenomics, proteomics, or multi-omics ... D in Computer Science, Bioinformatics, Computational Biology, Data Science, or a related field
... transcriptomics, and metagenomics of cancer. POSITION SUMMARY We are seeking an individual with ... Key instrumentation includes: an Illumina NovaSeq X Plus, Bruker Spatial Biology GeoMx and CosMx ...
... transcriptomics, and metagenomics of cancer. POSITION SUMMARY We are seeking an individual with ... Key instrumentation includes: an Illumina NovaSeq X Plus, Bruker Spatial Biology GeoMx and CosMx ...
... transcriptomics, and metagenomics of cancer. POSITION SUMMARY We are seeking an individual with ... Key instrumentation includes: an Illumina NovaSeq X Plus, Bruker Spatial Biology GeoMx and CosMx ...
... transcriptomics, and metagenomics of cancer. POSITION SUMMARY We are seeking an individual with ... Key instrumentation includes: an Illumina NovaSeq X Plus, Bruker Spatial Biology GeoMx and CosMx ...
... transcriptomics, and metagenomics of cancer. POSITION SUMMARY We are seeking an individual with ... Key instrumentation includes: an Illumina NovaSeq X Plus, Bruker Spatial Biology GeoMx and CosMx ...
... transcriptomics, and metagenomics of cancer. POSITION SUMMARY We are seeking an individual with ... Key instrumentation includes: an Illumina NovaSeq X Plus, Bruker Spatial Biology GeoMx and CosMx ...
... transcriptomics, and metagenomics of cancer. POSITION SUMMARY We are seeking an individual with ... Key instrumentation includes: an Illumina NovaSeq X Plus, Bruker Spatial Biology GeoMx and CosMx ...
... transcriptomics, and metagenomics of cancer. POSITION SUMMARY We are seeking an individual with ... Key instrumentation includes: an Illumina NovaSeq X Plus, Bruker Spatial Biology GeoMx and CosMx ...
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 Florida? For Computational Spatial Transcriptomics jobs in Florida, the most frequently searched job titles are:
What job categories do people searching Computational Spatial Transcriptomics jobs in Florida look for? The top searched job categories for Computational Spatial Transcriptomics jobs in Florida are:
- Phd Bioinformatics Salary
- Organoids Postdoc
- Trainee Postdoctoral Gene Therapy
- Temporary Molecular Biology Genetics
- Postdoc In Microbial Genomics
- Postdoctoral Position Parasitology Immunology
- Animal Microbiome Postdoc
- Cancer Organoid Scientist
- Remote Postdoctoral Molecular Biology
- Temporary Cryo Electron Microscopy
Assistant Scientist - Nephrology - Quantitative Health
University of FloridaGainesville, FL • On-site
Full-time
Posted 6 days ago
University Of Florida rating
7.2
Based on 108 frontline employees who took The Breakroom Quiz
334th of 537 rated colleges and universities
Job description
Job Summary:
The University of Florida is seeking an Assistant Scientist in the Department of Medicine to join the Computational Microscopy Imaging Laboratory. The successful candidate will engage in research focused on deep learning, digital pathology, and multi-omics data integration, particularly in relation to diabetic kidney disease.
Responsibilities:
• Demonstrated expertise in deep learning and AI frameworks (e.g., PyTorch, TensorFlow) and large-scale image analysis libraries (e.g., large_image by kitware)
• Hands-on experience with whole slide image analysis and computational pathology workflows
• Proficiency in Python and scientific computing libraries
• Experience with version control systems (e.g., Git/GitHub)
• Experience with high-performance computing (HPC) environments and large-scale data processing
• Contribute to cutting-edge research at the intersection of deep learning, digital pathology, and multi-omics data integration, with a strong emphasis on kidney disease, particularly, diabetic kidney disease.
• Engage in computational pathology and digital pathology, with application to kidney disease and diabetic kidney disease
• Develop deep learning and AI models for histological and microscopy image analysis
• Conduct whole slide image (WSI) analysis and quantitative microscopy
• Integrate spatial transcriptomics and multi-omics data
• Create high-performance computing and scalable biomedical data analysis pipelines
Qualifications:
Required:
• Applicants must hold a PhD in computer science, electrical engineering, biomedical engineering, or a closely related field
• Have completed at least 2–3 years of postdoctoral research experience
• Demonstrate a strong record of research productivity and scholarly achievement, as evidenced by peer-reviewed publications
• Demonstrated expertise in deep learning and AI frameworks (e.g., PyTorch, TensorFlow) and large-scale image analysis libraries (e.g., large_image by kitware)
• Hands-on experience with whole slide image analysis and computational pathology workflows
• Proficiency in Python and scientific computing libraries
• Experience with version control systems (e.g., Git/GitHub)
• Experience with high-performance computing (HPC) environments and large-scale data processing
Preferred:
• Prior research experience in kidney disease, with specific expertise in diabetic kidney disease highly preferred
• Experience with spatial transcriptomics and multi-omics data integration
• Familiarity with microscopy imaging modalities relevant to renal pathology
• Track record of or demonstrated potential for independent grant writing and funding acquisition
• Experience mentoring junior researchers, graduate students, or undergraduate trainees
• Strong written and verbal communication skills, with a collaborative research approach
Company:
University of Florida is a public land-grant research university in Gainesville, Florida. Founded in 1853, the company is headquartered in Gainesville, USA, with a team of 10001+ employees. The company is currently Late Stage.
The University of Florida is seeking an Assistant Scientist in the Department of Medicine to join the Computational Microscopy Imaging Laboratory. The successful candidate will engage in research focused on deep learning, digital pathology, and multi-omics data integration, particularly in relation to diabetic kidney disease.
Responsibilities:
• Demonstrated expertise in deep learning and AI frameworks (e.g., PyTorch, TensorFlow) and large-scale image analysis libraries (e.g., large_image by kitware)
• Hands-on experience with whole slide image analysis and computational pathology workflows
• Proficiency in Python and scientific computing libraries
• Experience with version control systems (e.g., Git/GitHub)
• Experience with high-performance computing (HPC) environments and large-scale data processing
• Contribute to cutting-edge research at the intersection of deep learning, digital pathology, and multi-omics data integration, with a strong emphasis on kidney disease, particularly, diabetic kidney disease.
• Engage in computational pathology and digital pathology, with application to kidney disease and diabetic kidney disease
• Develop deep learning and AI models for histological and microscopy image analysis
• Conduct whole slide image (WSI) analysis and quantitative microscopy
• Integrate spatial transcriptomics and multi-omics data
• Create high-performance computing and scalable biomedical data analysis pipelines
Qualifications:
Required:
• Applicants must hold a PhD in computer science, electrical engineering, biomedical engineering, or a closely related field
• Have completed at least 2–3 years of postdoctoral research experience
• Demonstrate a strong record of research productivity and scholarly achievement, as evidenced by peer-reviewed publications
• Demonstrated expertise in deep learning and AI frameworks (e.g., PyTorch, TensorFlow) and large-scale image analysis libraries (e.g., large_image by kitware)
• Hands-on experience with whole slide image analysis and computational pathology workflows
• Proficiency in Python and scientific computing libraries
• Experience with version control systems (e.g., Git/GitHub)
• Experience with high-performance computing (HPC) environments and large-scale data processing
Preferred:
• Prior research experience in kidney disease, with specific expertise in diabetic kidney disease highly preferred
• Experience with spatial transcriptomics and multi-omics data integration
• Familiarity with microscopy imaging modalities relevant to renal pathology
• Track record of or demonstrated potential for independent grant writing and funding acquisition
• Experience mentoring junior researchers, graduate students, or undergraduate trainees
• Strong written and verbal communication skills, with a collaborative research approach
Company:
University of Florida is a public land-grant research university in Gainesville, Florida. Founded in 1853, the company is headquartered in Gainesville, USA, with a team of 10001+ employees. The company is currently Late Stage.
What University Of Florida employees say
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Hours and flexibility
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About University of Florida
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The University of Florida is one of the top ranked public universities in the United States (ranked top 5 amongst public universities in 2023 US news and world report). It is one of only a few comprehensive universities, having medical, veterinary, dental, nursing, public health, and engineering disciplines all co-localized on the same, contiguous campus to facilitate interdisciplinary collaboration. Gainesville is located in the northern region of Florida, within 1-1.5 hours of each coast, and just 1.5-2 hours to Orlando and Tampa. It is a small to medium-sized city with a low cost of living, excellent public and private schools, and southern hospitality. While Gainesville is widely recognized as the home of the Gators, it is quickly becoming known as a center for innovation and a place with a lifestyle that's comfortable for families, yet attractive for young professionals.
Industry
Colleges, universities, and professional schools
Company size
5,001 - 10,000 Employees
Headquarters location
Gainesville, FL, US
Year founded
1853