Postdoctoral Researcher, Translational Cardio Genomics & ML

University of Arizona; Phoenix (AZ); Sur place; USD 56 000 - 66 000; Plein temps

Avantages offerts par ce poste

Full Benefits

Résumé du poste

The University of Arizona College of Medicine – Phoenix invites applications for a Postdoctoral Research Associate I in Translational Cardiovascular Research Center (Phoenix). The candidate will develop computational methods to study RNA species and networks in heart disease, collaborating with an interdisciplinary team.

The role emphasizes algorithm development, transcriptomics, sequencing data processing, and applying machine learning to large biomedical datasets, with opportunities for grants

Qualifications

• PhD in biomedical sciences, molecular biology, vascular biology, biochemistry, physiology, or related field.
• Experience with high-throughput sequencing data analysis and transcriptomics.
• Strong programming and data analysis skills for biomedical data.
• Experience with large-scale data analysis and publication records.
• Ability to work in an interdisciplinary team and communicate findings.

Responsabilités

• Develop and maintain detailed records of experiments, analyzing and writing findings.
• Assist with preparation of grants and manuscripts.
• Present manuscripts, oral presentations, posters, and research findings at conferences.
• Plan, coordinate, and conduct research, and serve as PI on sub-projects when appropriate.
• Generate new ideas and projects for development.
• Design and oversee experiments and data collection to ensure data integrity.
• Collect, analyze and interpret data; prepare reports and manuscripts for publication.

Connaissances

Python

R

Perl

Bioinformatics

Machine learning

Formation

PhD required

Outils

Ensembl

NCBI

Bowtie

SAMtools

Description du poste

The University of Arizona College of Medicine – Phoenix invites applications for a Postdoctoral Research Associate I in Translational Cardiovascular Research Center (Phoenix). The candidate will develop computational methods to study RNA species and networks in heart disease, collaborating with an interdisciplinary team.

The role emphasizes algorithm development, transcriptomics, sequencing data processing, and applying machine learning to large biomedical datasets, with opportunities for grants