Postdoctoral Researcher; Computational Phylogenetics

Regular, Full-Time, RCUH Non-Civil Service position with the Pacific Biosciences Research Center (PBRC), Pipes Laboratory, located on the University of Hawai'i at Manoa (UHM) in Honolulu, Hawai'i. Continuation of employment is dependent upon satisfactory work performance, program/operational needs, availability of funds, and compliance with applicable Federal/State laws.

$6,250 - $7,000/Mon.

Are you a Computational Biologist, Statistician, or Computer Scientist passionate about statistical phylogenetics and scalable algorithm design? The Pipes Laboratory at PBRC is seeking a highly skilled computational expert to drive innovation in analyzing ultra-large evolutionary trees. This role focuses on developing and implementing sophisticated algorithms, statistical models, and high-performance computing solutions to solve complex phylogenetic placement and inference problems in the context of environmental and public health surveillance.

• Lead the design, development, and optimization of scalable statistical phylogenetic methods
  for analyzing large-scale sequencing data from environmental and public health projects (e.g., wastewater surveillance, microbial ecology).
• Engineer novel algorithms and extend existing computational frameworks
  for complex tasks like species assignment and the creation of custom, scalable metabarcoding databases.
• Implement and deploy robust, efficient data analysis pipelines
  in a High-Performance Computing (HPC) environment, focusing on scalability and reproducibility.
• Apply rigorous statistical modeling and machine learning techniques
  to extract meaningful insights from complex biological datasets.
• Prepare manuscripts for peer-reviewed journals detailing novel computational/statistical methodologies and findings.
• Present technical findings and software/algorithm designs at scientific conferences and internal meetings.
• Collaborate with a multidisciplinary team, translating biological questions into well-defined computational and statistical problems.
• Mentor students in programming, data analysis, and computational best practices.
• Contribute to lab management, particularly regarding computational infrastructure and data resources, and assist in developing new research projects that leverage advanced computational and statistical approaches.

PhD from an accredited college or university in Computer Science, Statistics, Computational Biology
, or a related quantitative field (e.g., Physics, Applied Mathematics with strong computational/statistical thesis work). A PhD in Biology will be considered if accompanied by demonstrable, deep expertise and a significant track record in statistical phylogenetics, software development, algorithm design, and advanced statistical analysis.

One to three (1-3) years of experience focused on developing and applying computational algorithms, statistical models, and software tools for complex data analysis. Experience with Next-Generation Sequencing (NGS) data is highly advantageous, but strong foundational skills in programming, algorithm development, and statistics are paramount.

• Expert-level proficiency in programming languages such as C/C++, Python, and/or R.
• Strong theoretical and practical understanding of algorithm design, data structures, computational complexity, and software optimization techniques.
• In-depth knowledge of statistical principles, modeling techniques (e.g., regression, classification, clustering, Bayesian methods), and machine learning algorithms.
• Familiarity with computational biology concepts, particularly in the context of analyzing next-generation sequencing data, and phylogenetic analysis techniques (maximum likelihood, Bayesian) is a plus.

• Proven ability to independently design, develop, implement, and optimize sophisticated computational methods and statistical analyses for large-scale datasets.
• Strong experience in developing and managing computational pipelines in a High-Performance Computing (HPC) environment.
• Strong theoretical understanding of phylogenetic statistics, including substitution models, likelihood theory, Bayesian MCMC, and coalescent theory.
• Exceptional analytical, problem-solving, and debugging skills for complex computational and statistical challenges.
• Must be able to work independently, manage multiple technically demanding tasks effectively, and troubleshoot complex software and data issues.
• Must be able to complete the UH Title IX training within twelve (12) months from date of hire.
• Must be able to complete the UH Information Security Awareness Training (ISAT) within two (2) weeks from date of hire, and re-certify every twelve (12) months.

Ability to work at a computer for extended periods, performing tasks that require sustained focus, attention to detail, and precision.

As a condition of employment, employee will be subject to all applicable RCUH policies, procedures, and trainings and, as…