Postdoctoral Scholar Research

University of South Florida•Saint Petersburg, FL

7d•Onsite

About The Position

This bioinformatics position investigates marine metagenomes and metatranscriptomes from samples collected in the Pacific Ocean in conjunction with US GEOTRACES Cruises GP15 and GP17-OCE. The project's goal is to characterize phytoplankton realized niches and nutrient limitation status across the Pacific Ocean using amplicon sequencing, metagenomic, and metatranscriptomic analyses. These analyses will be combined with trace element and isotope (TEI) data to construct species distribution models (SDM) to infer realized niches, focusing on micro-nutrients like dissolved iron (dFe). The project will develop and combine 'omics-derived nutrient limitation indices (NLIs) with environmental data to train supervised machine learning (ML) models to extrapolate basin-wide patterns of nutrient limitation status for phytoplankton functional types (PFTs). The aim is to assess the feasibility of using Essential Ocean Variables (EOVs) from existing databases and remote-sensing products to produce dynamic maps of PFT-specific NLI using ML models.

Requirements

Bioinformatics skills
Experience analyzing metagenomic and metatranscriptomic samples
Understanding of nutrient limitation in phytoplankton
Familiarity with environmental data, including macro- and micronutrient concentrations and isotope values
Experience with supervised machine learning models (implied by the need to train them)
Ability to present research results at scientific meetings
Ability to lead publication of scientific results in peer-reviewed journals

Nice To Haves

Experience with amplicon sequencing
Experience with species distribution models (SDM)
Familiarity with Essential Ocean Variables (EOVs)
Experience with remote-sensing products

Responsibilities

Analyze metagenomic and metatranscriptomic samples to evaluate nutrient limitation of phytoplankton in the context of environmental data, including macro- and micronutrient concentrations and isotope values.
Develop NLIs using the analyzed data.
Work with a collaborator to use the NLIs and environmental data in training supervised ML models.
Present research results at national and international scientific meetings.
Lead the publication of scientific results in a peer-reviewed journal.
Participate in the preparation of at least one NSF proposal.
Attend workshops/seminars on Developing Individual Development Plan (IDP) and preparing teaching/research statements.