Postdoctoral Fellow

Jobtailor; Seattle (WA); On-site; USD 120,000 - 180,000; Full time

Job summary

Jobtailor seeks a highly qualified Biomedical Informatics researcher to lead experiments from design through first-authored publications. The role focuses on developing and validating machine learning and statistical methods to enhance clinical decision support and uncover disease mechanisms.

The candidate will perform multimodal analyses on genomic, phenomic and exposome data and collaborate with ISB colleagues and nationwide teams, contributing to cutting-edge interdisciplinary research.

Qualifications

• PhD in Biomedical Informatics or related field required.
• First authorship on one published, peer-reviewed article.
• Expertise in developing and validating machine learning models on biomedical data.
• Experience with multimodal modeling for clinical decision support or disease mechanisms.

Responsibilities

• Own biomedical informatics experiments from design through first-authorship publications.
• Develop and validate machine learning and statistical processes to improve clinical decision support or insights into disease mechanisms.
• Conduct multimodal modeling on high-dimensional longitudinal genomic, phenomic and exposome data.
• Collaborate on research projects with colleagues at ISB and nationwide.

Skills

Machine Learning

Statistical Processes

Data Preparation

Model Validation

Biomedical Data Analysis

Genomic Data Analysis

Phenomic Data Analysis

Exposome Data Analysis

Generative Models

Modeling And Analysis Methods

Education

PhD in Biomedical Informatics / related field

Job description

• Own biomedical informatics experiments, from initial design through first-authorship publications
• Develop and validate machine learning and statistical processes to improve clinical decision support or gain insights into mechanisms of disease
• Conduct multimodal modeling on high-dimensional longitudinal genomic, phenomic and exposome data
• Participate in collaborative research projects with colleagues at ISB and nationwide

Requirements

• Applicants should hold (or shortly be awarded) a PhD in Biomedical Informatics, Computational Biology, Information Science, Mathematics, Statistics, Data Science, Artificial Intelligence or a related field
• First authorship on one published, peer-reviewed article
• Expertise in developing and validating machine learning models on biomedical data, from data preparation through validation
• Experience with multimodal modeling for clinical decision support or for gaining insights into mechanisms of disease
• Strong verbal and written communication
• Ability to collaborate effectively in an interdisciplinary environment
• Preferred: experience with generative models
• Preferred: experience developing or validating new methods for modeling and analysis
• Preferred: insights into the temporal nature of disease and healthcare delivery: screening, diagnosis and treatment
• Preferred: experience with integrating semantic knowledge from databases, trees, graphs, ontologies or free text
• Applicants should include a CV, cover letter and two letters of recommendation

Demonstrates expertise in developing and validating machine learning models for biomedical data, with a focus on multimodal modeling and clinical decision support. Strong collaboration and communication skills are essential for interdisciplinary research projects.

Highest-signal resume keywords

• PhD In Biomedical Informatics
• Machine Learning Model Development
• Multimodal Modeling
• First Authorship On Published Article
• Collaboration In Interdisciplinary Environment

Hard Skills

• Machine Learning
• Statistical Processes
• Data Preparation
• Model Validation
• Biomedical Data Analysis
• Genomic Data Analysis
• Phenomic Data Analysis
• Exposome Data Analysis
• Generative Models
• Modeling And Analysis Methods

Soft Skills

• Strong Verbal Communication
• Strong Written Communication

Industry Keywords

• Biomedical Informatics
• Computational Biology
• Clinical Decision Support
• Peer-Reviewed Publication
• Interdisciplinary Research