Bioinformatics Research Assistant: ML for Regulatory Genomics

Karolinska Institutet; Solna kommun; On-site; SEK 301,320 - 412,920; Full time

Job summary

Karolinska Institutet in Stockholm, Sweden seeks a research assistant to analyze single-cell and spatial multiome data, and to support ML-driven gene therapy research for CNS repair. You will join a multidisciplinary team in a translational biology setting with strong experimental collaboration.

We value an MSc in bioinformatics or related fields, experience with scRNA-seq/spatial genomics, and proficiency in Python or R, plus keen interest in machine learning applications to genomics.

Qualifications

• MSc degree in bioinformatics, computational biology, biotechnology, or related field.
• Experience in analysis of single-cell and/or spatial genomics datasets (scRNA-seq, scATAC-seq, multiome).
• Proficiency in R and/or Python for data analysis and visualization.
• Familiarity with gene regulation, epigenomics, and enhancer biology is desirable.
• Interest in machine learning-driven approaches applied to biological data.

Responsibilities

• Analyze single-cell and spatial multiome datasets (transcriptomics and epigenomics) from mouse models and human patient samples to study gene regulation underlying injury response mechanisms.
• Support the development and application of deep learning models to predict activity and specificity of gene therapy constructs.
• Collaborate with experimental researchers to link computational predictions with genomics validation strategies.

Skills

scRNA-seq analysis

Python/R

English proficiency

Deep learning interest

Gene regulation

Education

MSc in bioinformatics

Job description

Karolinska Institutet in Stockholm, Sweden seeks a research assistant to analyze single-cell and spatial multiome data, and to support ML-driven gene therapy research for CNS repair. You will join a multidisciplinary team in a translational biology setting with strong experimental collaboration.

We value an MSc in bioinformatics or related fields, experience with scRNA-seq/spatial genomics, and proficiency in Python or R, plus keen interest in machine learning applications to genomics.