Postdoctoral Fellow – AI, Computational Biology, & Systems Biology
for sections like About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. Only include sections that have real specific content. Remove boilerplate, application instructions, etc. Must preserve all substantive info.
We need to parse the posting: It includes:
- Position specifics: description of role, research focus, etc. Could be "About the role" or "Responsibilities". We need to decide.
- Research Areas: list of projects.
- Candidate Profile: description of desired background, skills.
- Research environment: description.
- Required Qualifications: list.
- Preferred qualifications: list.
- How to Apply: but we should remove application instructions (the instructions about how to apply). The instruction says remove application instructions. So we should not include "How to Apply" section.
- Benefits: mention of benefits package, link to benefits page. That's substantive: we can include Benefits section.
- Pay: not mentioned. So no Pay section.
- Schedule: not mentioned. So no Schedule.
- Also there is mention of term position length discussed during interview, continuation based on need etc. That could be under About the role or Responsibilities? Might be part of position specifics. Could be included in About the role.
We must not repeat job title as heading. No Job ID.
We need to use for section headings. Use - for any list of duties/requirements/benefits. Use
for prose.
We need to preserve all substantive info, not invent or summarize away detail. So we need to keep details like specific tools, methods, etc.
We must remove cookie/navigation/legal/EEO/equal-opportunity/scam-warning boilerplate, duplicated lines, and application instructions. So we should strip out the EEO statement, background checks, campus security crime stats, etc. Also remove the "How to Apply" section (since it's application instructions). Also remove the "Approval of remote and hybrid work is not guaranteed..." etc. Also remove the "This is a term position; length of the term will be discussed during the interview process. Continuation past the term length discussed will be based on university need, performance, and/or availability of funding." That is substantive? It's about term position. Might be considered part of About the role. It's not boilerplate; it's specific to the position. So we should keep that info.
We also need to keep "Background Checks/Clearances" maybe? That's boilerplate? It's about employment requiring background check. Might be considered boilerplate but it's a condition of employment. The instruction says remove cookie/navigation/legal/EEO/equal-opportunity/scam-warning boilerplate. Background check might be considered legal boilerplate. Safer to remove it? The instruction says remove legal boilerplate. So we should remove "BACKGROUND CHECKS/CLEARANCES" section.
Also remove "CAMPUS SECURITY CRIME STATISTICS", "EEO IS THE LAW", etc.
Also remove "Federal Contractors Labor Law Poster PA State Labor Law Poster Penn State Policies Copyright Information Hotlines".
Now we need to structure.
Potential sections:
- About the role (or Responsibilities): includes position specifics description, term position note, research focus, opportunities.
- Responsibilities: maybe list of duties? The posting doesn't have explicit responsibilities list; it's more about what the fellow will do: develop and apply computational approaches, etc. Could be under About the role.
- Requirements: maybe Required Qualifications.
- Qualifications: maybe Preferred qualifications.
- Skills: maybe desired computational skills.
- Benefits: benefits package.
We need to avoid thin sections. Let's see what we have:
Substantive content blocks:
1. Position specifics paragraph: "We are seeking a highly motivated Postdoctoral Fellow in AI, Computational Biology, and Systems Biology, to join an interdisciplinary research program focused on understanding the molecular mechanisms underlying complex biological mechanisms using the integration of large-scale multi-omic datasets. The successful candidate will develop and apply computational and statistical approaches to integrate genomic, transcriptomic, epigenomic, proteomic, and physiological phenotypes to identify molecular mechanisms, regulatory networks, disease-associated pathways, and potential therapeutic targets. The position provides an opportunity to work at the intersection of physiology, AI, computational biology, and systems biology, with access to increasingly large and diverse multi-omic datasets. A major focus of the position will be the development of computational frameworks to decipher organ-cross talk by connecting molecular dataset (multi-omics) to cellular phenotypes, tissue-specific regulatory programs, and physiological responses to exercise. Candidates with expertise in multi-omics integration, single-cell and long-read transcriptomics are particularly encouraged to apply."
2. Research Areas bullet list (list of projects). This is a list.
3. Candidate Profile paragraph: "We are seeking candidates with a Ph.D., M.D., or equivalent doctoral degree in bioinformatics, computational biology, systems biology, genomics, genetics, biostatistics, biomedical engineering, molecular biology, or a related discipline with documented experience using the appropriate methodology described above including but not limited to long-read RNA-seq analysis (PacBio HiFi sequencing), transcript and isoform discovery, alternative splicing and isoform characterization, genome/transcriptome alignment and annotation, single-cell and gene regulatory network discovery using scRNAseq, and integration of single-cell data with bulk and multi-omic datasets Desired computational skills: Strong programming experience in Python and/or R is expected. Experience with Linux/HPC environments, workflow development, statistical modeling, machine learning, and reproducible computational pipelines is highly desirable. Experience with tools and resources such as Scanpy, Seurat, GENIE3, SCENIC, DESeq2, STAR, minimap2, Salmon, kallisto, Bioconductor, Ensembl, GTEx, and protein-interaction databases would be advantageous. Candidates with experience developing new computational methods rather than exclusively applying existing pipelines are particularly encouraged to apply."
4. Research environment paragraph: "The fellow will work in a highly interdisciplinary environment involving computational biologists, molecular biologists, physiologists, and data scientists. The position provides opportunities to develop independent research directions while contributing to collaborative projects involving large-scale human and experimental datasets. The candidate will lead and co-lead publications, present research at national and international conferences, develop grant proposals and fellowship applications, collaborate with experimental investigators to generate and test computationally derived hypotheses, and develop expertise in emerging multi-omic and AI-enabled approaches to biomedical research"
5. Required Qualifications bullet list: "Ph.D., M.D., or equivalent doctoral degree in a relevant field Demonstrated research experience in computational biology, bioinformatics, genomics, or a related field Strong programming skills in R and/or Python Experience analyzing high-throughput biological datasets Strong quantitative, analytical, and problem-solving skills Excellent written and oral communication skills"
6. Preferred qualifications: "Experience with long-read transcriptomics/PacBio HiFi, single-cell transcriptomics, gene regulatory network inference, and working with HPC environments and reproducible computational workflows"
7. Benefits: "Penn State provides a competitive benefits package for full-time employees designed to support both personal and professional well-being. For more detailed information, please visit our Benefits Page. (Note: For Postdoctoral benefits, please see our Postdoctoral Benefits page.)"
We also have term position note: "This is a term position; length of the term will be discussed during the interview process. Continuation past the term length discussed will be based on university need, performance, and/or availability of funding." This could be placed under About the role.
We also have "Research Areas" list.
We need to decide headings.
Potential headings:
- About the role (includes position specifics, term note, research focus)
- Responsibilities (maybe list of duties: develop and apply computational approaches, etc.) But we already have that in About the role. Could split.
- Research Areas (list)
- Candidate Profile (maybe Qualifications)
- Required Qualifications
- Preferred qualifications
- Benefits
We need to avoid too many thin sections. But we have substantive content for each.
We must use
for section headings. Use - for any list of duties/requirements/benefits. Use
for prose.
Thus for each section we need to decide if content is prose or list.
Let's craft:
not needed; just output HTML fragment.
We'll produce:
About the role
We are seeking a highly motivated Postdoctoral Fellow in AI, Computational Biology, and Systems Biology, to join an interdisciplinary research program focused on understanding the molecular mechanisms underlying complex biological mechanisms using the integration of large-scale multi-omic datasets. The successful candidate will develop and apply computational and statistical approaches to integrate genomic, transcriptomic, epigenomic, proteomic, and physiological phenotypes to identify molecular mechanisms, regulatory networks, disease-associated pathways, and potential therapeutic targets. The position provides an opportunity to work at the intersection of physiology, AI, computational biology, and systems biology, with access to increasingly large and diverse multi-omic datasets. A major focus of the position will be the development of computational frameworks to decipher organ-cross talk by connecting molecular dataset (multi-omics) to cellular phenotypes, tissue-specific regulatory programs, and physiological responses to exercise. Candidates with expertise in multi-omics integration, single-cell and long-read transcriptomics are particularly encouraged to apply.
This is a term position; length of the term will be discussed during the interview process. Continuation past the term length discussed will be based on university need, performance, and/or availability of funding.
Then maybe Research Areas
- Multi-omics data integration across genomics, transcriptomics, proteomics, epigenomics, and metabolomics
- long-read RNA sequencing and rRNA isoform discovery using PacBio HiFi and other long-read platforms
- single-cell RNA-seq analysis and cell-type-specific molecular profiling
- gene regulatory network inference using approaches such as GENIE3, SCENIC, and related network-based methods
- develop computational approaches for therapeutic target discovery and prioritization
- integration of heterogeneous datasets to generate and test mechanistic biological hypotheses
Then Candidate Profile
We are seeking candidates with a Ph.D., M.D., or equivalent doctoral degree in bioinformatics, computational biology, systems biology, genomics, genetics, biostatistics, biomedical engineering, molecular biology, or a related discipline with documented experience using the appropriate methodology described above including but not limited to long-read RNA-seq analysis (PacBio HiFi sequencing), transcript and isoform discovery, alternative splicing and isoform characterization, genome/transcriptome alignment and annotation, single-cell and gene regulatory network discovery using scRNAseq, and integration of single-cell data with bulk and multi-omic datasets. Desired computational skills: Strong programming experience in Python and/or R is expected. Experience with Linux/HPC environments, workflow development, statistical modeling, machine learning, and reproducible computational pipelines is highly desirable. Experience with tools and resources such as Scanpy, Seurat, GENIE3, SCENIC, DESeq2, STAR, minimap2, Salmon, kallisto, Bioconductor, Ensembl, GTEx, and protein-interaction databases would be advantageous. Candidates with experience developing new computational methods rather than exclusively applying existing pipelines are particularly encouraged to apply.
Then Required Qualifications
- Ph.D., M.D., or equivalent doctoral degree in a relevant field
- Demonstrated research experience in computational biology, bioinformatics, genomics, or a related field
- Strong programming skills in R and/or Python
- Experience analyzing high-throughput biological datasets
- Strong quantitative, analytical, and problem-solving skills
- Excellent written and oral communication skills
Then Preferred qualifications
- Experience with long-read transcriptomics/PacBio HiFi
- single-cell transcriptomics
- gene regulatory network inference
- working with HPC environments and reproducible computational workflows
Then Benefits
Penn State provides a competitive benefits package for full-time employees designed to support both personal
- for any list of duties/requirements/benefits. Use
for prose. We need to preserve all substantive info, not invent or summarize away detail. So we need to keep details like specific tools, methods, etc. We must remove cookie/navigation/legal/EEO/equal-opportunity/scam-warning boilerplate, duplicated lines, and application instructions. So we should strip out the EEO statement, background checks, campus security crime stats, etc. Also remove the "How to Apply" section (since it's application instructions). Also remove the "Approval of remote and hybrid work is not guaranteed..." etc. Also remove the "This is a term position; length of the term will be discussed during the interview process. Continuation past the term length discussed will be based on university need, performance, and/or availability of funding." That is substantive? It's about term position. Might be considered part of About the role. It's not boilerplate; it's specific to the position. So we should keep that info. We also need to keep "Background Checks/Clearances" maybe? That's boilerplate? It's about employment requiring background check. Might be considered boilerplate but it's a condition of employment. The instruction says remove cookie/navigation/legal/EEO/equal-opportunity/scam-warning boilerplate. Background check might be considered legal boilerplate. Safer to remove it? The instruction says remove legal boilerplate. So we should remove "BACKGROUND CHECKS/CLEARANCES" section. Also remove "CAMPUS SECURITY CRIME STATISTICS", "EEO IS THE LAW", etc. Also remove "Federal Contractors Labor Law Poster PA State Labor Law Poster Penn State Policies Copyright Information Hotlines". Now we need to structure. Potential sections: - About the role (or Responsibilities): includes position specifics description, term position note, research focus, opportunities. - Responsibilities: maybe list of duties? The posting doesn't have explicit responsibilities list; it's more about what the fellow will do: develop and apply computational approaches, etc. Could be under About the role. - Requirements: maybe Required Qualifications. - Qualifications: maybe Preferred qualifications. - Skills: maybe desired computational skills. - Benefits: benefits package. We need to avoid thin sections. Let's see what we have: Substantive content blocks: 1. Position specifics paragraph: "We are seeking a highly motivated Postdoctoral Fellow in AI, Computational Biology, and Systems Biology, to join an interdisciplinary research program focused on understanding the molecular mechanisms underlying complex biological mechanisms using the integration of large-scale multi-omic datasets. The successful candidate will develop and apply computational and statistical approaches to integrate genomic, transcriptomic, epigenomic, proteomic, and physiological phenotypes to identify molecular mechanisms, regulatory networks, disease-associated pathways, and potential therapeutic targets. The position provides an opportunity to work at the intersection of physiology, AI, computational biology, and systems biology, with access to increasingly large and diverse multi-omic datasets. A major focus of the position will be the development of computational frameworks to decipher organ-cross talk by connecting molecular dataset (multi-omics) to cellular phenotypes, tissue-specific regulatory programs, and physiological responses to exercise. Candidates with expertise in multi-omics integration, single-cell and long-read transcriptomics are particularly encouraged to apply." 2. Research Areas bullet list (list of projects). This is a list. 3. Candidate Profile paragraph: "We are seeking candidates with a Ph.D., M.D., or equivalent doctoral degree in bioinformatics, computational biology, systems biology, genomics, genetics, biostatistics, biomedical engineering, molecular biology, or a related discipline with documented experience using the appropriate methodology described above including but not limited to long-read RNA-seq analysis (PacBio HiFi sequencing), transcript and isoform discovery, alternative splicing and isoform characterization, genome/transcriptome alignment and annotation, single-cell and gene regulatory network discovery using scRNAseq, and integration of single-cell data with bulk and multi-omic datasets Desired computational skills: Strong programming experience in Python and/or R is expected. Experience with Linux/HPC environments, workflow development, statistical modeling, machine learning, and reproducible computational pipelines is highly desirable. Experience with tools and resources such as Scanpy, Seurat, GENIE3, SCENIC, DESeq2, STAR, minimap2, Salmon, kallisto, Bioconductor, Ensembl, GTEx, and protein-interaction databases would be advantageous. Candidates with experience developing new computational methods rather than exclusively applying existing pipelines are particularly encouraged to apply." 4. Research environment paragraph: "The fellow will work in a highly interdisciplinary environment involving computational biologists, molecular biologists, physiologists, and data scientists. The position provides opportunities to develop independent research directions while contributing to collaborative projects involving large-scale human and experimental datasets. The candidate will lead and co-lead publications, present research at national and international conferences, develop grant proposals and fellowship applications, collaborate with experimental investigators to generate and test computationally derived hypotheses, and develop expertise in emerging multi-omic and AI-enabled approaches to biomedical research" 5. Required Qualifications bullet list: "Ph.D., M.D., or equivalent doctoral degree in a relevant field Demonstrated research experience in computational biology, bioinformatics, genomics, or a related field Strong programming skills in R and/or Python Experience analyzing high-throughput biological datasets Strong quantitative, analytical, and problem-solving skills Excellent written and oral communication skills" 6. Preferred qualifications: "Experience with long-read transcriptomics/PacBio HiFi, single-cell transcriptomics, gene regulatory network inference, and working with HPC environments and reproducible computational workflows" 7. Benefits: "Penn State provides a competitive benefits package for full-time employees designed to support both personal and professional well-being. For more detailed information, please visit our Benefits Page. (Note: For Postdoctoral benefits, please see our Postdoctoral Benefits page.)" We also have term position note: "This is a term position; length of the term will be discussed during the interview process. Continuation past the term length discussed will be based on university need, performance, and/or availability of funding." This could be placed under About the role. We also have "Research Areas" list. We need to decide headings. Potential headings: - About the role (includes position specifics, term note, research focus) - Responsibilities (maybe list of duties: develop and apply computational approaches, etc.) But we already have that in About the role. Could split. - Research Areas (list) - Candidate Profile (maybe Qualifications) - Required Qualifications - Preferred qualifications - Benefits We need to avoid too many thin sections. But we have substantive content for each. We must use
for section headings. Use
- for any list of duties/requirements/benefits. Use
for prose. Thus for each section we need to decide if content is prose or list. Let's craft: not needed; just output HTML fragment. We'll produce:
About the role
We are seeking a highly motivated Postdoctoral Fellow in AI, Computational Biology, and Systems Biology, to join an interdisciplinary research program focused on understanding the molecular mechanisms underlying complex biological mechanisms using the integration of large-scale multi-omic datasets. The successful candidate will develop and apply computational and statistical approaches to integrate genomic, transcriptomic, epigenomic, proteomic, and physiological phenotypes to identify molecular mechanisms, regulatory networks, disease-associated pathways, and potential therapeutic targets. The position provides an opportunity to work at the intersection of physiology, AI, computational biology, and systems biology, with access to increasingly large and diverse multi-omic datasets. A major focus of the position will be the development of computational frameworks to decipher organ-cross talk by connecting molecular dataset (multi-omics) to cellular phenotypes, tissue-specific regulatory programs, and physiological responses to exercise. Candidates with expertise in multi-omics integration, single-cell and long-read transcriptomics are particularly encouraged to apply.
This is a term position; length of the term will be discussed during the interview process. Continuation past the term length discussed will be based on university need, performance, and/or availability of funding.
Then maybeResearch Areas
- Multi-omics data integration across genomics, transcriptomics, proteomics, epigenomics, and metabolomics
- long-read RNA sequencing and rRNA isoform discovery using PacBio HiFi and other long-read platforms
- single-cell RNA-seq analysis and cell-type-specific molecular profiling
- gene regulatory network inference using approaches such as GENIE3, SCENIC, and related network-based methods
- develop computational approaches for therapeutic target discovery and prioritization
- integration of heterogeneous datasets to generate and test mechanistic biological hypotheses
Candidate Profile
We are seeking candidates with a Ph.D., M.D., or equivalent doctoral degree in bioinformatics, computational biology, systems biology, genomics, genetics, biostatistics, biomedical engineering, molecular biology, or a related discipline with documented experience using the appropriate methodology described above including but not limited to long-read RNA-seq analysis (PacBio HiFi sequencing), transcript and isoform discovery, alternative splicing and isoform characterization, genome/transcriptome alignment and annotation, single-cell and gene regulatory network discovery using scRNAseq, and integration of single-cell data with bulk and multi-omic datasets. Desired computational skills: Strong programming experience in Python and/or R is expected. Experience with Linux/HPC environments, workflow development, statistical modeling, machine learning, and reproducible computational pipelines is highly desirable. Experience with tools and resources such as Scanpy, Seurat, GENIE3, SCENIC, DESeq2, STAR, minimap2, Salmon, kallisto, Bioconductor, Ensembl, GTEx, and protein-interaction databases would be advantageous. Candidates with experience developing new computational methods rather than exclusively applying existing pipelines are particularly encouraged to apply.
ThenRequired Qualifications
- Ph.D., M.D., or equivalent doctoral degree in a relevant field
- Demonstrated research experience in computational biology, bioinformatics, genomics, or a related field
- Strong programming skills in R and/or Python
- Experience analyzing high-throughput biological datasets
- Strong quantitative, analytical, and problem-solving skills
- Excellent written and oral communication skills
Preferred qualifications
- Experience with long-read transcriptomics/PacBio HiFi
- single-cell transcriptomics
- gene regulatory network inference
- working with HPC environments and reproducible computational workflows
Benefits
Penn State provides a competitive benefits package for full-time employees designed to support both personal
- for any list of duties/requirements/benefits. Use