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Postdoctoral Fellow-MSH-32910-036
- Type
- Full time
- Posted
- 16D AGO
About the role
Postdoctoral Fellow — Human Genomics
Position Overview
We are seeking a highly motivated Postdoctoral Fellow with extensive experience in statistical genetics/genetic epidemiology to join our research program focused on the genetics and genomics of complex traits and rare disorders under the supervision of Dr. Nathalie Chami.
Our research integrates large-scale human genetic and genomic data with electronic health records, proteomics, multi-omics, and other deeply phenotyped datasets to understand the genetic architecture and biological heterogeneity of complex disease. A major focus of the lab is moving beyond gene discovery to understand disease penetrance, subtypes, mechanisms, and comorbidities, with the long-term goal of advancing precision medicine.
The successful candidate will have the opportunity to lead projects using large, diverse biobanks and population-based cohorts, including datasets with genome-wide genotyping, whole-exome and whole-genome sequencing, longitudinal electronic health records, proteomics, and other molecular and clinical phenotypes.
The position is particularly well suited for a candidate interested in developing an independent research program in human genetics, statistical genetics, genetic epidemiology, or computational genomics.
Research Areas
Potential research areas include:
- Genetic discovery for obesity, diabetes, cardiovascular disease, and related cardiometabolic traits
- Genome-wide association studies (GWAS) and fine-mapping
- Rare-variant association analyses using whole-exome and whole-genome sequencing
- Integration of rare and common genetic variation
- Polygenic risk scores and genetic modifiers of disease penetrance
- Genetic and molecular characterization of disease subtypes
- Multi-ancestry and cross-population genetic analyses
- Integration of genomics with proteomics and other multi-omics data
- Genetics of longitudinal clinical outcomes and electronic health record-derived phenotypes
- Integration of genomic and imaging-derived traits
- Causal inference and translation of genetic discoveries into biological and clinical insight
Required Qualifications
Candidates should have:
- A PhD in human genetics, statistical genetics, genetic epidemiology, bioinformatics, computational biology, biostatistics, or a closely related field
- Strong quantitative and analytical skills
- Experience analyzing human genetic or genomic data
- Proficiency in R
- Experience working in Unix/Linux computational environments
- Strong understanding of statistical principles relevant to genetic and epidemiologic research
- Ability to independently analyze data, critically interpret results, and troubleshoot analytical problems
- Strong scientific writing and communication skills
- Demonstrated ability to work both independently and collaboratively
- Experience in statistical genetic including GWAS , Whole-exome or whole-genome sequencing analysis
- Polygenic risk scores
- Fine-mapping, colocalization, or other post-GWAS analyses
- Large-scale biobank datasets
- High-performance computing and reproducible computational workflows
- A strong publication record and evidence of the ability to take scientific ownership of a project are highly valued.
Desired Attributes
We are looking for someone who is:
- Scientifically curious and creative
- Highly motivated and intellectually independent
- Interested in asking biologically and clinically meaningful questions using human genomic data
- Rigorous and thoughtful in quantitative analysis
- Proactive and able to move projects forward independently
- Collaborative and comfortable working in multidisciplinary teams
- Organized, dependable, and committed to reproducible research
- Interested in developing toward scientific independence
Research Environment and Career Development
The fellow will work in a highly collaborative environment at the intersection of human genetics, genomics, epidemiology, computational biology, and precision medicine. The position provides access to large-scale genomic and deeply phenotyped datasets and opportunities to collaborate with investigators across multiple disciplines.
A major emphasis will be placed on the fellow’s career development and scientific independence. Fellows will be encouraged to develop their own research questions, lead manuscripts, present their work at scientific meetings, participate in grant writing, and pursue fellowship and career-development funding.
Application Materials
Interested candidates should submit:
- Curriculum vitae
- Brief cover letter describing research experience and future research interests
- Contact information for 2–3 references
Compensation Statement
The Mount Sinai Health System (MSHS) provides salary ranges that comply with the New York City Law on Salary Transparency in Job Advertisements. The salary range for the role is $74,692.00 - $80,000.00 Annually. Actual salaries depend on a variety of factors, including experience, education, and operational need. The salary range or contractual rate listed does not include bonuses/incentive, differential pay or other forms of compensation or benefits.
SPOC-UAW Local 4100 at Icahn School of Medicine (Post Docs), J18 - AI and Human Health - ISM, Icahn School of Medicine
Responsibilities
The Postdoctoral Fellow will:
- Lead independent research projects from study design through analysis, interpretation, and publication
- Analyze large-scale genomic datasets, including GWAS, exome sequencing, whole-genome sequencing, and rare-variant data
- Integrate genomic data with clinical, EHR, proteomic, multi-omic, and other phenotypic datasets
- Develop and implement rigorous statistical and computational approaches for human genetic studies
- Interpret genetic findings in the context of disease biology and potential clinical relevance
- Prepare manuscripts for publication in peer-reviewed journals and present findings at national and international scientific meetings
- Contribute to grant applications and development of new research directions
- Collaborate with investigators across genetics, epidemiology, clinical medicine, bioinformatics, data science, and related disciplines
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