Postdoc in Bioinformatics for Translational Breast Cancer

Karolinska Institutet; Solna kommun; On-site; SEK 480,000 - 600,000; Full time

Job summary

Karolinska Institutet seeks a postdoctoral researcher in bioinformatics to lead analyses of genomic, transcriptomic and methylation data in breast cancer, with a focus on precision medicine. You will develop pipelines and contribute to multi-omics integration using machine learning within a collaborative team.

The role involves handling single-cell and spatial omics datasets and collaborating with KI, KTH, and SciLifeLab on translational projects.

Qualifications

• PhD required or equivalent
• Proficiency in scripting (R/Python) and Linux CLI
• Experience with sequencing data (DNA/RNA/methylation) analysis
• Experience with spatial transcriptomics, single-cell sequencing and multi-omics data integration is advantageous
• Good knowledge of molecular biology and cancer; interest in learning more
• Excellent English communication, written and verbal

Responsibilities

• Analyze genomic, transcriptomic, and DNA methylation data, including somatic mutation and copy-number calling
• Develop and maintain pipelines for genomic and transcriptomic analysis
• Work with single-cell and spatial omics datasets; perform multi-omics integration
• Analyze large-scale clinical and registry data with biostatistical methods
• Collaborate with clinicians, bioinformaticians, computer scientists and biologists

Skills

R/Python scripting

Linux command line

Genomic data analysis

Single-cell sequencing

Spatial transcriptomics

Multi-omics integration

AI software

Data management

Biostatistics

Education

PhD in bioinformatics / computational biology / molecular biology / computer science

Tools

Computational pathology tools

Job description

Karolinska Institutet seeks a postdoctoral researcher in bioinformatics to lead analyses of genomic, transcriptomic and methylation data in breast cancer, with a focus on precision medicine. You will develop pipelines and contribute to multi-omics integration using machine learning within a collaborative team.

The role involves handling single-cell and spatial omics datasets and collaborating with KI, KTH, and SciLifeLab on translational projects.