Postdoctoral Research Fellow in Alzheimer's Disease Research

The Division of Translational Informatics at the University of New Mexico Health Sciences Center is seeking a talented and motivated Postdoctoral Research Fellow to join our team engaged in Alzheimer's Disease and Related Dementias (ADRD) research. This fully funded position, available until July 31, 2026, offers a unique opportunity to be at the forefront of research that could revolutionize our understanding and prevention of ADRD.

Our pioneering project aims to transform the landscape of ADRD research by enhancing causal inference from longitudinal data. We're leveraging advanced causal feature selection methods that integrate comprehensive knowledge from the biomedical literature and ontologies. As a key member of our research team, you'll contribute to the development and application of these innovative methodologies to analyze health data, working with complex longitudinal datasets to uncover new insights into ADRD.

The successful candidate will possess a Ph.D. in Epidemiology, Biostatistics, Computer Science, or a related field, with a strong background in causal inference and machine learning. Proficiency in programming (R, Python) and experience with large-scale biomedical data analysis is essential, as is the ability to communicate complex ideas effectively, both in writing and verbally. The ideal applicant will demonstrate a track record of peer-reviewed publications and the ability to work independently while contributing to a collaborative research environment.

Responsibilities:

• (25%) Conduct large-scale epidemiological analyses of AD risk factors and interventions: Analyze longitudinal datasets using advanced statistical models (e.g., time-varying Cox models, structural equation modeling). Apply causal inference methods, including DAGs and propensity score techniques. Experience with the OMOP Common Data Model is strongly desired. Familiarity with large-scale repositories (All of Us, UK Biobank) preferred.
• (20%) Develop and apply computational methods for knowledge discovery in AD research: Refine Knowledge-Based Discovery strategies for ADRD risk factor identification. Utilize and fine-tune Large Language Models on biomedical corpora (>1M articles). Implement NLP techniques for information extraction from scientific literature. Develop databases and knowledge graphs using NLP and/or LLMs to synthesize knowledge from biomedical literature and biomedical ontologies.
• (15%) Optimize data management and integration for AD research: Lead database enhancement adhering to FAIR principles. Implement data validation protocols and standardize formats across cohorts. Ensure accuracy and standardization of data pivotal to AD research. Collaborate on solutions for managing high-dimensional omics data.
• (15%) Develop and validate advanced causal inference methodologies: Design and implement novel causal feature selection algorithms for AD research. Explore cutting-edge approaches in causal machine learning. Validate methods using both simulated data and real-world cohorts (n > 10,000).
• (10%) Collaborate with interdisciplinary teams: Engage with experts in neurology, epidemiology, and bioinformatics to advance project goals. Contribute to significant grant applications, particularly NIH R01 or equivalent funding mechanisms. (10%) Disseminate research findings: Prepare manuscripts for high-impact journals. Attend and present at relevant workshops and research conferences. Synthesize findings into clear, compelling narratives that highlight innovative methodological approaches.
• (5%) Maintain research documentation: Keep detailed reports of workflows and procedures to ensure reproducibility and facilitate knowledge transfer within the research group.

Benefits:

* Opportunity to lead a segment of a high-profile research project with the potential to impact AD prevention and treatment strategies significantly.

* Access to state-of-the-art research facilities and datasets, including longitudinal data from renowned repositories like the UK Biobank.

* Engagement with a network of leading researchers and opportunities for multidisciplinary collaboration.

* Support for professional development, including conference attendance, workshops, and training in advanced research methodologies.

* Competitive salary and benefits package commensurate with experience and qualifications.

Embark on a postdoctoral journey that promises to advance your career and contribute to meaningful change in Alzheimer's Disease research. Apply now to join our team!

Minimum Qualifications:

• Ph.D. in Epidemiology, Biostatistics, Neuroscience, Bioinformatics, Public Health, or a closely related field, with a strong emphasis on AD research
  

Preferred Qualifications:

• Demonstrated experience with causal inference methods and proficiency in statistical software (e.g., R, SAS, Python)
• A track record of peer-reviewed publications in biomedical research or related areas
• Excellent communication and organizational skills, with a proven ability to manage complex research projects
• Experience with large-scale data analysis and a deep understanding of observational study designs
• A demonstrated commitment to diversity, equity, inclusion, and student success, as well as working with broadly diverse communities

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a) a detailed cover letter highlighting your research interests, AD research experience, and project fit

b) Curriculum Vitae, including a list of publications, research achievements, and contact information for three references

3) A brief research proposal (1-2 pages) outlining your envisioned contributions to the project

Inquiries may be directed to Scott A. Malec, PhD, Assistant Professor, Division of Translational Informatics, Department of Internal Medicine, University of New Mexico

smalec@salud.unm.edu

The University of New Mexico is committed to hiring and retaining a diverse workforce. We are an Equal Opportunity Employer, making decisions without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, veteran status, disability, or any other protected class.