Post-Doctoral Fellow (Biostatistics and Cancer Epidemiology)
UNSW · Los Angeles County, CA · 1 mo ago
Analyst$133k–$156k/yrFull-time
We need to output HTML fragment with sections: headings for sections like About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. Only include sections if posting has real specific content.
We need to parse the job posting.
Key info:
- Title: Post-Doctoral Fellow (Biostatistics and Cancer Epidemiology) – but we must not repeat job title as heading.
- Employment Type: Full-time (35 hours a week) – schedule? Could be schedule section? It's about hours. Could be Schedule section.
- Duration: 2-Year Fixed Term (possible extension, funding dependant) – maybe not a section.
- Remuneration: Academic Level B $133k - $156k base (+17% super, plus leave loading) – Pay section.
- Work rights: Visa sponsorship not available; must hold full rights to work in Australia – could be part of Requirements? Not a heading.
- Location: UNSW South-West Sydney Clinical School, Liverpool Hospital (Hybrid working) – maybe part of About the role.
- The Opportunity description: research-focused position within CCORE etc. Could be About the role.
- Key Skills And Experience Required: list bullet points.
- UNSW Benefits and Culture: list benefits.
- How To Apply etc: we must remove application instructions.
- Pre-Employment Checks etc: remove boilerplate.
- Enquiries etc: remove.
- Equity diversity statement: remove.
Thus we need sections: About the role (maybe summarizing opportunity), Responsibilities (maybe not explicit), Requirements (maybe combine key skills), Qualifications (maybe PhD etc), Skills (maybe statistical skills), Benefits (UNSW Benefits), Pay (remuneration), Schedule (full-time 35 hours a week). Also maybe Duration? Not a standard heading; we could embed in About the role.
We must not create sections with filler like "not specified". So only include sections where we have concrete content.
Let's extract:
About the role: The Opportunity paragraph: research-focused position working within CCORE, Ingham Institute, South-West Sydney Clinical School, UNSW. They will have opportunity to refine expertise in health services and equity research and contribute to University's standing as national leader. The Post-Doctoral Fellow will have opportunity to work on statistical analyses of large-scale administrative datasets examining inequalities in evidence-based care for cancer. The research will involve data linkages of Australian Census and other Commonwealth health and socioeconomic datasets to NSW health data. This is a unique opportunity to work within a multidisciplinary team alongside clinicians and staff specialists who provide practical, real-world insights and expert guidance to support the interpretation and analysis of complex data. Associate Professor Mei Ling Yap Dr Md Mijanur Rahman (these are contacts? maybe not needed). We'll include that as About the role.
Responsibilities: Not explicitly listed; maybe implied from opportunity: statistical analyses, data linkages, etc. Could treat as Responsibilities. But we need to avoid duplication. Could combine with About the role. However we need to decide. The instruction: Prefer a few substantive sections over many thin ones. So we could have About the role (includes opportunity and responsibilities). Then Requirements/Qualifications/Skills separate.
Requirements: maybe the list of key skills and experience required. That includes PhD, contributed to grant applications, interest in reducing inequalities, high level statistical and data analysis skills, evidence of strong data management skills, experience managing large linked admin datasets using Stata/R/SAS/python, emerging publication track record, ability to engage with audiences.
Qualifications: maybe PhD etc. But we can separate: Qualifications (PhD), Skills (statistical and data analysis, data management, programming, communication). Benefits: UNSW Benefits and Culture list.
Pay: Remuneration.
Schedule: Full-time (35 hours a week). Could also mention hybrid working? That's location.
We must not repeat job title as heading. So no
for sections like About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. Only include sections if posting has real specific content.
We need to parse the job posting.
Key info:
- Title: Post-Doctoral Fellow (Biostatistics and Cancer Epidemiology) – but we must not repeat job title as heading.
- Employment Type: Full-time (35 hours a week) – schedule? Could be schedule section? It's about hours. Could be Schedule section.
- Duration: 2-Year Fixed Term (possible extension, funding dependant) – maybe not a section.
- Remuneration: Academic Level B $133k - $156k base (+17% super, plus leave loading) – Pay section.
- Work rights: Visa sponsorship not available; must hold full rights to work in Australia – could be part of Requirements? Not a heading.
- Location: UNSW South-West Sydney Clinical School, Liverpool Hospital (Hybrid working) – maybe part of About the role.
- The Opportunity description: research-focused position within CCORE etc. Could be About the role.
- Key Skills And Experience Required: list bullet points.
- UNSW Benefits and Culture: list benefits.
- How To Apply etc: we must remove application instructions.
- Pre-Employment Checks etc: remove boilerplate.
- Enquiries etc: remove.
- Equity diversity statement: remove.
Thus we need sections: About the role (maybe summarizing opportunity), Responsibilities (maybe not explicit), Requirements (maybe combine key skills), Qualifications (maybe PhD etc), Skills (maybe statistical skills), Benefits (UNSW Benefits), Pay (remuneration), Schedule (full-time 35 hours a week). Also maybe Duration? Not a standard heading; we could embed in About the role.
We must not create sections with filler like "not specified". So only include sections where we have concrete content.
Let's extract:
About the role: The Opportunity paragraph: research-focused position working within CCORE, Ingham Institute, South-West Sydney Clinical School, UNSW. They will have opportunity to refine expertise in health services and equity research and contribute to University's standing as national leader. The Post-Doctoral Fellow will have opportunity to work on statistical analyses of large-scale administrative datasets examining inequalities in evidence-based care for cancer. The research will involve data linkages of Australian Census and other Commonwealth health and socioeconomic datasets to NSW health data. This is a unique opportunity to work within a multidisciplinary team alongside clinicians and staff specialists who provide practical, real-world insights and expert guidance to support the interpretation and analysis of complex data. Associate Professor Mei Ling Yap Dr Md Mijanur Rahman (these are contacts? maybe not needed). We'll include that as About the role.
Responsibilities: Not explicitly listed; maybe implied from opportunity: statistical analyses, data linkages, etc. Could treat as Responsibilities. But we need to avoid duplication. Could combine with About the role. However we need to decide. The instruction: Prefer a few substantive sections over many thin ones. So we could have About the role (includes opportunity and responsibilities). Then Requirements/Qualifications/Skills separate.
Requirements: maybe the list of key skills and experience required. That includes PhD, contributed to grant applications, interest in reducing inequalities, high level statistical and data analysis skills, evidence of strong data management skills, experience managing large linked admin datasets using Stata/R/SAS/python, emerging publication track record, ability to engage with audiences.
Qualifications: maybe PhD etc. But we can separate: Qualifications (PhD), Skills (statistical and data analysis, data management, programming, communication). Benefits: UNSW Benefits and Culture list.
Pay: Remuneration.
Schedule: Full-time (35 hours a week). Could also mention hybrid working? That's location.
We must not repeat job title as heading. So no Post-Doctoral Fellow
.
We need to output only HTML fragment, no /. Use for section headings. Use - for any list of duties/requirements/benefits. Use
for prose.
We must preserve ALL substantive information; do not invent or summarize away detail. So we need to keep details like numbers, percentages, etc.
Let's craft sections:
About the role
This is a research-focused position working within the Collaboration for Cancer Outcomes, Research and Evaluation (CCORE), Ingham Institute, South‑West Sydney Clinical School, UNSW. The Post‑Doctoral Fellow will refine expertise in health services and equity research, contribute to the University’s standing as a national leader in the discipline, and work on statistical analyses of large‑scale administrative datasets examining inequalities in evidence‑based care for cancer. The research involves data linkages of Australian Census and other Commonwealth health and socioeconomic datasets to NSW health data, undertaken within a multidisciplinary team alongside clinicians and staff specialists who provide practical, real‑world insights and expert guidance to support the interpretation and analysis of complex data.
Maybe also mention hybrid working? Could be in About the role or separate. We'll add location/hybrid in About the role: "Based at UNSW South‑West Sydney Clinical School, Liverpool Hospital with hybrid working arrangements."
Add that.
Responsibilities
We could list duties: statistical analyses of large-scale admin datasets, data linkages, etc. But we already covered in About. To avoid duplication, maybe we skip Responsibilities and just have About. But the instruction says use headings only when posting has real specific content. We have specific content for responsibilities? The posting didn't list responsibilities separately; it's embedded in opportunity. So maybe we skip Responsibilities.
Requirements
We could list the key skills and experience required as a list.
But we also have Qualifications and Skills headings. Could combine: Requirements includes qualifications and skills. However we have separate headings for Qualifications and Skills. The instruction says use headings for those categories if content exists. We have content for Qualifications (PhD etc) and Skills (statistical, data management, programming, communication). So we can split.
Thus:
Qualifications
A PhD in Biostatistics, Epidemiology, Data Science, or a related discipline (or relevant work experience).
But also "Have contributed to competitive grant‑funding applications." That's more experience than qualification. Could be under Requirements.
Better: Qualifications: PhD. Requirements: list of experience items (grant contributions, interest in reducing inequalities, publication track record, ability to engage audiences). Skills: statistical and data analysis skills, data management, programming languages, communication.
Let's parse the bullet list:
- A PhD in Biostatistics, Epidemiology, Data Science, or related discipline and/or relevant work experience
- Have contributed to competitive grant-funding applications.
- An interest in research aimed at reducing health system inequalities and improving cancer outcomes.
- High level statistical and data analysis skills in advanced analytic methods including causal inference and prognostic/risk prediction modelling.
- It will be essential to have evidence of strong data management skills and experience managing large linked administrative datasets using statistical software including Stata, R or SAS, python.
- Have an emerging publication track record with high impact publication(s) within biostatistics, cancer and health services research or another related field.
- Ability to engage with a variety of audiences, tailoring communication to suit the relevant audience.
We can split:
Qualifications: PhD (and/or relevant work experience). Could also mention "and/or relevant work experience" as part of qualification.
Requirements: maybe the grant contributions, interest in reducing inequalities, publication track record, ability to engage audiences.
Skills: high level statistical and data analysis skills (causal inference, prognostic/risk prediction), strong data management skills, experience managing large linked admin datasets using Stata/R/SAS/python.
But we also have "High level statistical and data analysis skills in advanced analytic methods including causal inference and prognostic/risk prediction modelling." That's skill.
"It will be essential to have evidence of strong data management skills and experience managing large linked administrative datasets using statistical software including Stata, R or SAS, python." That's skill.
Thus Skills section includes those.
Requirements: grant contributions, interest in reducing inequalities, publication track record, ability to engage audiences.
We also have "Full details can be found in the position description." That's boilerplate; ignore.
Now Benefits: UNSW Benefits and Culture list:
- Additional 3 days leave during Christmas holidays
- Career development opportunities
- Up to 50% discount on UNSW courses
- Flexible 17% Superannuation contributions, additional leave-loading payments and salary sacrifice.
- Discounts and entitlements (retail, education, fitness passport)
We need to preserve all details.
Pay: Remuneration: Academic Level B $133k - $156k base (+17% super, plus leave loading)
Schedule: Full-time (35 hours a week). Also hybrid working? Already in About.
We could also include Duration: 2-Year Fixed Term (possible extension, funding dependant) maybe as part of About or separate? Not a standard heading; we could add to About.
Let's craft About to include location, hybrid, duration.
Now produce HTML.
We must not include any markdown fences. Just raw HTML.
Let's write:
About the role
This is a research‑focused position based at the UNSW South‑West Sydney Clinical School, Liverpool Hospital (hybrid working) within the Collaboration for Cancer Outcomes, Research and Evaluation (CCORE), Ingham Institute. The appointment is for a 2‑year fixed term (possible extension, funding dependent). The Post‑Doctoral Fellow will refine expertise in health services and equity research, contribute to the University’s standing as a national leader in the discipline, and work on statistical analyses of large‑scale administrative datasets examining inequalities in evidence‑based care for cancer. The research involves data linkages of Australian Census and other Commonwealth health and socioeconomic datasets to NSW health data, undertaken within a multidisciplinary team alongside clinicians and staff specialists who provide practical, real‑world insights and expert guidance to support the interpretation and analysis of complex data.
Qualifications
A PhD in Biostatistics, Epidemiology, Data Science, or a related discipline (or relevant work experience).
Requirements
- Experience contributing to competitive grant‑funding applications.
- A demonstrated interest in research aimed at reducing health system inequalities and improving cancer outcomes.
- An emerging publication track record with high‑impact publication(s) in biostatistics, cancer, health services research or a related field.
- Ability to engage with a variety of audiences, tailoring communication to suit the relevant audience.
Skills
- High‑level statistical and data analysis skills, including advanced analytic methods such as causal inference and prognostic/risk prediction modelling.
- Strong data management skills and experience managing large linked administrative datasets using statistical software (Stata, R, SAS, Python).
Benefits
- Additional 3 days leave during Christmas holidays.
- Career development opportunities.
- Up to 50% discount on UNSW courses.
- Flexible 17% superannuation contributions, additional leave‑loading payments and salary sacrifice options.
- Discounts and entitlements (retail, education, fitness passport).
Pay
Academic Level B salary range: $133,000 – $156,000 base per annum, plus 17% superannuation and leave loading.
Schedule
Full‑time position (35 hours per week) with hybrid working arrangements.
Check we didn't repeat job title. Good.
We omitted duplicated lines, boilerplate, application instructions, EEO, etc.
We preserved all substantive info: location, hybrid,
- for any list of duties/requirements/benefits. Use
for prose. We must preserve ALL substantive information; do not invent or summarize away detail. So we need to keep details like numbers, percentages, etc. Let's craft sections:
About the role
This is a research-focused position working within the Collaboration for Cancer Outcomes, Research and Evaluation (CCORE), Ingham Institute, South‑West Sydney Clinical School, UNSW. The Post‑Doctoral Fellow will refine expertise in health services and equity research, contribute to the University’s standing as a national leader in the discipline, and work on statistical analyses of large‑scale administrative datasets examining inequalities in evidence‑based care for cancer. The research involves data linkages of Australian Census and other Commonwealth health and socioeconomic datasets to NSW health data, undertaken within a multidisciplinary team alongside clinicians and staff specialists who provide practical, real‑world insights and expert guidance to support the interpretation and analysis of complex data.
Maybe also mention hybrid working? Could be in About the role or separate. We'll add location/hybrid in About the role: "Based at UNSW South‑West Sydney Clinical School, Liverpool Hospital with hybrid working arrangements." Add that.Responsibilities
We could list duties: statistical analyses of large-scale admin datasets, data linkages, etc. But we already covered in About. To avoid duplication, maybe we skip Responsibilities and just have About. But the instruction says use headings only when posting has real specific content. We have specific content for responsibilities? The posting didn't list responsibilities separately; it's embedded in opportunity. So maybe we skip Responsibilities.Requirements
We could list the key skills and experience required as a list. But we also have Qualifications and Skills headings. Could combine: Requirements includes qualifications and skills. However we have separate headings for Qualifications and Skills. The instruction says use headings for those categories if content exists. We have content for Qualifications (PhD etc) and Skills (statistical, data management, programming, communication). So we can split. Thus:Qualifications
A PhD in Biostatistics, Epidemiology, Data Science, or a related discipline (or relevant work experience).
But also "Have contributed to competitive grant‑funding applications." That's more experience than qualification. Could be under Requirements. Better: Qualifications: PhD. Requirements: list of experience items (grant contributions, interest in reducing inequalities, publication track record, ability to engage audiences). Skills: statistical and data analysis skills, data management, programming languages, communication. Let's parse the bullet list: - A PhD in Biostatistics, Epidemiology, Data Science, or related discipline and/or relevant work experience - Have contributed to competitive grant-funding applications. - An interest in research aimed at reducing health system inequalities and improving cancer outcomes. - High level statistical and data analysis skills in advanced analytic methods including causal inference and prognostic/risk prediction modelling. - It will be essential to have evidence of strong data management skills and experience managing large linked administrative datasets using statistical software including Stata, R or SAS, python. - Have an emerging publication track record with high impact publication(s) within biostatistics, cancer and health services research or another related field. - Ability to engage with a variety of audiences, tailoring communication to suit the relevant audience. We can split: Qualifications: PhD (and/or relevant work experience). Could also mention "and/or relevant work experience" as part of qualification. Requirements: maybe the grant contributions, interest in reducing inequalities, publication track record, ability to engage audiences. Skills: high level statistical and data analysis skills (causal inference, prognostic/risk prediction), strong data management skills, experience managing large linked admin datasets using Stata/R/SAS/python. But we also have "High level statistical and data analysis skills in advanced analytic methods including causal inference and prognostic/risk prediction modelling." That's skill. "It will be essential to have evidence of strong data management skills and experience managing large linked administrative datasets using statistical software including Stata, R or SAS, python." That's skill. Thus Skills section includes those. Requirements: grant contributions, interest in reducing inequalities, publication track record, ability to engage audiences. We also have "Full details can be found in the position description." That's boilerplate; ignore. Now Benefits: UNSW Benefits and Culture list: - Additional 3 days leave during Christmas holidays - Career development opportunities - Up to 50% discount on UNSW courses - Flexible 17% Superannuation contributions, additional leave-loading payments and salary sacrifice. - Discounts and entitlements (retail, education, fitness passport) We need to preserve all details. Pay: Remuneration: Academic Level B $133k - $156k base (+17% super, plus leave loading) Schedule: Full-time (35 hours a week). Also hybrid working? Already in About. We could also include Duration: 2-Year Fixed Term (possible extension, funding dependant) maybe as part of About or separate? Not a standard heading; we could add to About. Let's craft About to include location, hybrid, duration. Now produce HTML. We must not include any markdown fences. Just raw HTML. Let's write:About the role
This is a research‑focused position based at the UNSW South‑West Sydney Clinical School, Liverpool Hospital (hybrid working) within the Collaboration for Cancer Outcomes, Research and Evaluation (CCORE), Ingham Institute. The appointment is for a 2‑year fixed term (possible extension, funding dependent). The Post‑Doctoral Fellow will refine expertise in health services and equity research, contribute to the University’s standing as a national leader in the discipline, and work on statistical analyses of large‑scale administrative datasets examining inequalities in evidence‑based care for cancer. The research involves data linkages of Australian Census and other Commonwealth health and socioeconomic datasets to NSW health data, undertaken within a multidisciplinary team alongside clinicians and staff specialists who provide practical, real‑world insights and expert guidance to support the interpretation and analysis of complex data.
Qualifications
A PhD in Biostatistics, Epidemiology, Data Science, or a related discipline (or relevant work experience).
Requirements
- Experience contributing to competitive grant‑funding applications.
- A demonstrated interest in research aimed at reducing health system inequalities and improving cancer outcomes.
- An emerging publication track record with high‑impact publication(s) in biostatistics, cancer, health services research or a related field.
- Ability to engage with a variety of audiences, tailoring communication to suit the relevant audience.
Skills
- High‑level statistical and data analysis skills, including advanced analytic methods such as causal inference and prognostic/risk prediction modelling.
- Strong data management skills and experience managing large linked administrative datasets using statistical software (Stata, R, SAS, Python).
Benefits
- Additional 3 days leave during Christmas holidays.
- Career development opportunities.
- Up to 50% discount on UNSW courses.
- Flexible 17% superannuation contributions, additional leave‑loading payments and salary sacrifice options.
- Discounts and entitlements (retail, education, fitness passport).
Pay
Academic Level B salary range: $133,000 – $156,000 base per annum, plus 17% superannuation and leave loading.
Schedule
Full‑time position (35 hours per week) with hybrid working arrangements.
Check we didn't repeat job title. Good. We omitted duplicated lines, boilerplate, application instructions, EEO, etc. We preserved all substantive info: location, hybrid,