Postdoc in Computational Transcriptomics - Long Read RNA-Seq

A*STAR RESEARCH ENTITIES; Singapore; Sur place; SGD 60 000 - 90 000; Plein temps

Avantages offerts par ce poste

Competitive salary

Generous benefits

Résumé du poste

Genome Institute of Singapore (A*STAR) invites applications for a postdoctoral fellow to develop computational methods for population-scale long read RNA-Seq data. The role includes identifying RNA modifications from direct RNA-Seq data and analyzing transcript expression, alternative promoters, and transcription start sites from single-cell and spatial long read RNA-Seq data.

You will work closely with experimental and clinical collaborators and have access to the latest sequencing technologies

Qualifications

• PhD in Bioinformatics, Computer Science, Statistics, Genomics, or a related discipline.
• Good programming skills and strong interest in methods development.
• Experience in algorithms development, transcriptome analysis, RNA modifications, statistics, ML, long read RNA-Sequencing or direct RNA-Sequencing.
• Publication record with a first author publication (pre-prints accepted).
• Excellent communication skills.

Responsabilités

• Develop computational methods for population-scale long read RNA-Seq data.
• Identify RNA modifications from direct RNA-Seq data and analyze transcript expression, alternative promoters and TSSs.
• Collaborate with experimental and clinical collaborators and industry partners.

Connaissances

Programming

Algorithms

Transcriptome analysis

RNA modifications

Statistics

Machine learning

Communication

Formation

PhD in Bioinformatics, Computer Science, Statistics, Genomics

Outils

Bambu

m6Anet

xPore

Description du poste

Genome Institute of Singapore (A*STAR) invites applications for a postdoctoral fellow to develop computational methods for population-scale long read RNA-Seq data. The role includes identifying RNA modifications from direct RNA-Seq data and analyzing transcript expression, alternative promoters, and transcription start sites from single-cell and spatial long read RNA-Seq data.

You will work closely with experimental and clinical collaborators and have access to the latest sequencing technologies