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Postdoctoral Associate - Human Genetics

University of Miami
Miami, USA
Full-time
Mid
Onsite
Discovered 6 days ago
linuxpython

Full Job Posting

Experience:

  • Doctoral or postdoctoral research experience analyzing high-throughput sequencing data, with a record of first-author publications. Experience with long-read sequencing data, tumor-normal somatic variant analysis, or germline variant interpretation is strongly preferred. Familiarity with cloud-based analysis of controlled-access datasets (e.g., dbGaP, All of Us) and with reproducible workflow development is also desirable. Prior work in cancer genetics or hereditary cancer susceptibility is an advantage but is not required.
  • Knowledge, Skills and Attitudes:
  • Proficiency in Python and/or R and in Linux shell scripting, with working knowledge of a workflow manager and containerization.
  • Working knowledge of standard genomic file formats (FASTQ, BAM/CRAM, VCF, BED) and of tools for alignment, variant calling, structural variant detection, and phasing.
  • Familiarity with variant annotation and interpretation resources and with ACMG/AMP variant classification principles.
  • Sound statistical skills, including regression modeling, multiple testing correction, and diagnostic accuracy metrics; experience with DNA methylation or other epigenomic data is a plus.
  • Commitment to reproducible research practices, including version control, documentation, and transparent reporting of methods.
  • Ability to communicate computational findings clearly to clinical and non-computational audiences, and to write effectively for publication.
  • Ability to work independently while contributing to a collaborative, clinically embedded research team
  • The University of Miami offers competitive salaries and a comprehensive benefits package including medical, dental, tuition remission and more.
  • UHealth-University of Miami Health System, South Florida's only university-based health system, provides leading-edge patient care powered by the ground breaking research and medical education at the Miller School of Medicine. As an academic medical center, we are proud to serve South Florida, Latin America and the Caribbean. Our physicians represent more than 100 specialties and sub-specialties, and have more than one million patient encounters each year. Our tradition of excellence has earned worldwide recognition for outstanding teaching, research and patient care. We're the challenge you've been looking for.
  • The University of Miami is an Equal Opportunity Employer. Applicants and employees are protected from discrimination based on certain categories protected by Federal law.
  • Job Status:
  • Full time
  • Employee Type:
  • Staff
  • No previous experience required
  • Refer to department description for applicable certification requirements
  • Learning Agility: Ability to learn new procedures, technologies, and protocols, and adapt to changing priorities and work demands.
  • Teamwork: Ability to work collaboratively with others and contribute to a team environment.
  • Technical Proficiency: Skilled in using office software, technology, and relevant computer applications.
  • Communication: Strong and clear written and verbal communication skills for interacting with colleagues and stakeholders.
  • This is a core job profile description and is not reflective of all duties that may be assigned to a specific position in each individual department. The above statements are intended to describe the general nature and primary responsibilities of this core job profile. Specific duties and tasks may vary based upon departmental needs. Other duties may be assigned to the above consistent with the knowledge, skills, and abilities required for the job.
  • Department Specific Functions
  • The Post Doctoral Associate will join the research program of Nicholas Borja, MD, at Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine. The position applies bioinformatics and statistical genomics to investigate mechanisms of cancer susceptibility, genotype–phenotype correlations, and biomarkers for cancer risk and early detection. Active projects include exploring the molecular etiologies of multiple primary cancers, methods to distinguish constitutional mosaicism from clonal hematopoiesis, and validation of novel gene-disease associations.
  • Develop, validate, and maintain reproducible pipelines for short-read and long-read DNA sequencing and RNA sequencing, from raw data processing and quality control through variant detection, annotation, and biological interpretation.
  • Integrate germline and tumor sequencing to investigate cancer susceptibility, including structural variation, constitutional mosaicism, clonal hematopoiesis, loss of heterozygosity, and mutational signatures.
  • Analyze RNA data to evaluate gene expression, aberrant splicing, gene fusions, and the functional consequences of candidate genomic variants; incorporate DNA methylation and other molecular data where relevant.
  • Construct and evaluate polygenic risk scores and investigate genotype–phenotype relationships using institutional cohorts and large genomic datasets, accounting for ancestry and relevant clinical and environmental factors.
  • Contribute to studies of cancer early detection assays through analysis of sequencing data, evaluation of genomic and epigenomic biomarkers, and assessment of assay performance.
  • Collaborate with clinical investigators and institutional bioinformatics teams to develop research questions, interpret findings, prepare manuscripts and grant applications, and mentor trainees.
  • Maintain documented, version-controlled workflows and secure data practices.
  • Department Specific Qualifications
  • Education: Ph.D. in bioinformatics, computational biology, genomics, human genetics, biostatistics, computer science, or a related discipline.

Preferred Qualifications

  • Doctoral or postdoctoral research experience analyzing high-throughput sequencing data, with a record of first-author publications. Experience with long-read sequencing data, tumor-normal somatic variant analysis, or germline variant interpretation is strongly preferred. Familiarity with cloud-based analysis of controlled-access datasets (e.g., dbGaP, All of Us) and with reproducible workflow development is also desirable. Prior work in cancer genetics or hereditary cancer susceptibility is an advantage but is not required.
  • Sound statistical skills, including regression modeling, multiple testing correction, and diagnostic accuracy metrics; experience with DNA methylation or other epigenomic data is a plus.

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