Postdoctoral Fellow in Single-Cell and Spatial Bioinformatics

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for section headings like About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. We must include only sections that have real specific content. The posting includes description, responsibilities, qualifications, etc. No explicit Benefits, Pay, Schedule. So we should not create those sections. We need sections: About the role (maybe from description), Responsibilities, Qualifications (maybe split into Required and Preferred?), Skills? The instructions: Use headings for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule). They gave examples but we can choose appropriate headings based on content. We must not repeat job title as heading. Also we should not include Job ID etc. We need to preserve all substantive information. Remove boilerplate like application instructions? Actually they said remove application instructions. So we should omit the "Application Instructions" part. Also remove cookie/navigation/legal/EEO etc. Not present. We need to include sections only when posting has real specific content. So we can have:

About the role

with description paragraph.

Responsibilities

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  Qualifications

  maybe split into Required and Preferred? Could have subheadings? But only allowed headings are those listed; we could still use

  for Required Qualifications and

  for Preferred Qualifications? Might be okay but they said use those headings for sections; we can have multiple sections with those headings. Could do Required Qualifications and Preferred Qualifications as separate

  sections. Also Skills maybe not needed if covered in qualifications. Could omit. We must not invent content like "commensurate with experience". Not present. We need to preserve all substantive information: include details about research focus, components, computational environment, research environment, career development, etc. Some of that may fit in About the role or Responsibilities. We need to ensure we don't miss any details: The posting includes: - Opportunity description: postdoc in Dana T. Graves lab, investigating inflammatory mechanisms affecting skin, mucosa, skeleton, periodontium in diabetes, aging, other pathologic conditions. Two components: leading bioinformatic analyses of scRNA-seq and 10x Genomics Xenium spatial transcriptomic datasets; validation via multiplex immunofluorescence and quantitative image-analysis to establish spatial distribution etc. - Research Focus: diabetes and other factors alter cell signaling, differentiation, immune-stromal interactions; projects span multiple tissues, disease models, species, experimental platforms. - Component 1 details: lead bioinformatic analyses to identify disease-associated cell states, transcriptional programs, spatially organized cellular responses. - Component 2 – Other Opportunities: develop additional lab skills, orthogonal validation approaches such as immunofluorescence and quantitative image analysis; focus on characterizing spatial distribution, molecular and cellular localization, tissue organization in vivo specimens. - Across bioinformatics component: investigate spatial signaling networks, cell-cell communication, temporal changes in cell state; projects may include regulatory network inference, pseudotime analysis, machine-learning approaches. - When informative, findings may be integrated to relate molecular and cellular states to adhesion-molecule distribution. - Key Responsibilities (list): - Lead bioinformatic analyses of scRNA-seq and 10x Genomics Xenium spatial transcriptomic datasets. - Serve as project leader for quantitative image analysis of in vivo specimens to characterize spatial distribution, cellular localization, tissue organization of adhesion molecules. - Develop rigorous, reproducible computational workflows. - Perform quality control, data integration, cell annotation, differential expression analyses. - Conduct pathway, trajectory, state-transition, ligand-receptor analyses. - Integrate multiomic, cross-species, cross-cohort datasets. - Integrate transcriptomic data with imaging, histologic, phenotypic measurements. - Generate clear figures and communicate findings to computational and experimental collaborators. - Contribute to analytical strategy and biological interpretation. - Present findings and prepare first-author manuscripts. - Contribute to grant development and collaborative studies. - Computational Environment: primary environment uses R, Seurat, related tools; other validated methods may be used; image-analysis component uses quantitative imaging and spatial-analysis tools appropriate. - Research Environment and Career Development: Graves lab integrates computational discovery with in vivo models, human specimens, histology, flow cytometry, immunofluorescence, in vitro validation. Experimental systems include genetically engineered mouse models, models of diabetes and aging, primary mouse and human cell cultures, molecular perturbation studies. Fellow will have substantial intellectual ownership of both components, leadership of scRNA-seq and Xenium bioinformatic analyses and project leadership for image-analysis study. Responsibilities include selecting analytical approaches, leading analysis, interpreting and presenting findings, preparing first-author manuscripts and participation in grant writing. Development of independent fellowship grants encouraged. Dr. Graves provides direct scientific mentoring and regular project guidance. Fellow will collaborate with investigators and shared-resource specialists across UPenn. Penn core facilities support single-cell and spatial genomics, biostatistics, imaging, histology, quantitative analysis. Position offers training at interface of computational biology, genomics, diabetes, inflammation, tissue repair, mouse genetics, translational research. Goal: support scientific independence, strong publications, grant development, preparation for academic or industry career. - Qualifications: - Required: PhD, MD, DMD, DVM, or equivalent doctoral degree in relevant field. - Hands-on experience with bioinformatic analysis of scRNA-seq or spatial transcriptomic data, sufficient to lead single-cell/Xenium bioinformatics component. - Strong proficiency in R or Python and modern single-cell analysis workflows. - Ability to interpret analytical results in biological and disease contexts. - Ability to work independently and collaborate effectively across disciplines. - Strong scientific writing and communication skills. - Relevant fields include bioinformatics, computational biology, genomics, biostatistics, systems biology, molecular or cell biology, immunology, bioengineering, diabetes biology, skeletal biology, computer science, statistics, data science, or related discipline. - Preferred Qualifications Include Experience In The Following Areas: - Spatial transcriptomics. - Seurat and related R packages. - Multiomic, multi-species, or cross-cohort integration. - Trajectory or pseudotime analysis, cell-cell communication analysis, or regulatory network inference. - Quantitative or spatial image analysis of immunofluorescence, histologic, or related in vivo imaging datasets, with experience sufficient to independently lead an image-analysis project. - In vitro or in vivo validation experiments. - Integration of molecular and imaging data. - Selected Publications list (maybe we can include as part of About the role or a separate section? 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  Selected Publications

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  Selected Publications

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  About the role

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  Responsibilities

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  Qualifications

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  Research Environment and Career Development

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  Research Environment and Career Development

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  . However, they say "Prefer a few substantive sections over many thin ones." So we could combine. We could have About the role covering description, research focus, computational environment, research environment and career development. That may become long but okay. Responsibilities separate. Qualifications separate. We should not include Benefits, Pay, Schedule. Now craft the HTML. Let's write About the role paragraph summarizing: The Opportunity ... etc. Need to preserve all substantive information: we can include multiple paragraphs. We'll include:

  The Opportunity A postdoctoral position is available in the laboratory of Dr. Dana T. Graves at the University of Pennsylvania. The fellow will investigate inflammatory mechanisms affecting the skin, mucosa, skeleton, and periodontium in the context of diabetes, aging, and other pathologic conditions. The position comprises two primary, complementary components: (1) leading bioinformatic analyses of single-cell RNA sequencing (scRNA-seq) and 10x Genomics Xenium spatial transcriptomic datasets; and (2) validation through techniques such as multiplex immunofluorescence and quantitative image-analysis to establish spatial distribution and molecular, cellular and tissue organization of in vivo specimens. The goal is to define mechanisms of disease and identify potential therapeutic targets.

  Research Focus Our research investigates how diabetes and other factors alter cell signaling, differentiation, and immune-stromal interactions. Projects span multiple tissues, disease models, species, and experimental platforms.

  Component 1 - Single-cell and spatial genomics bioinformatics: The fellow will lead bioinformatic analyses of scRNA-seq and 10x Genomics Xenium spatial transcriptomic datasets to identify disease-associated cell states, transcriptional programs, and spatially organized cellular responses.

  Component 2 – Other Opportunities. The fellow will have opportunities to develop additional laboratory skills, including orthogonal validation approaches such as immunofluorescence and quantitative image analysis. These activities will focus on characterizing spatial distribution, molecular and cellular localization, and tissue organization in vivo specimens.

  Across the bioinformatics component, the fellow will investigate spatial signaling networks, cell-cell communication, and temporal changes in cell state. Projects may include regulatory network inference, pseudotime analysis, and machine-learning approaches when scientifically appropriate. When informative, findings from the bioinformatics and image-analysis components may be integrated to relate molecular and cellular states to adhesion-molecule distribution.

  Computational Environment For the single-cell and spatial genomics bioinformatics component, a major focus will be analysis of 10x Genomics Xenium spatial transcriptomic and scRNA-seq datasets. The primary computational environment uses R, Seurat, and related tools. Other validated methods may be used when they improve analytical rigor or interpretation. The image-analysis component will use quantitative imaging and spatial-analysis tools appropriate to the specimens, imaging modalities, and scientific questions.

  Research Environment and Career Development The Graves laboratory integrates computational discovery with in vivo models, human specimens, histology, flow cytometry, immunofluorescence, and in vitro validation. Experimental systems include genetically engineered mouse models, models of diabetes and aging, primary mouse and human cell cultures, and molecular perturbation studies. The fellow will have substantial intellectual ownership of both components, including leadership of scRNA-seq and Xenium bioinformatic analyses and project leadership for the image-analysis study. Responsibilities include selecting analytical approaches, leading analysis, interpreting and presenting findings, preparing first-author manuscripts and