Company:
Biogen
Location: Cambridge
Closing Date: 28/11/2024
Hours: Full Time
Type: Permanent
Job Requirements / Description
Job Description
This application is for a 6-month student role from January - June 2025. Resume review begins in October 2024.
Our team is Computational Biology. The aim of our team is to perform high quality computational analysis on genomics/transcriptomics datasets, standardize NGS data analysis pipelines, and work on cutting edge AI/ML tools and technologies. We collaborate closely with all discovery teams to perform computational analysis of NGS datasets. These includes analysis of RNAseq, snRNAseq, spatial transcriptomics data to perform target identification, engagement and assessment.
Position Description
Detailed description of role including but not limited to:
Leverage cutting-edge machine learning models to improve 1.single cell RNA-seq analysis, 2. Image segmentation for spatial transcriptomics
Implement machine learning algorithms and develop computational tools/packages
Attend scheduled team bi-weekly meetings
Give bi-weekly updates of progress.
Example projects may include:
Utilizing computer vision and LLM models to enhance segmentation in spatial transcriptomics data and refine cell calling across diverse spatial transcriptomics platforms and tissue types
Developing and maintaining computational tools (gpu computing, scRNA-seq analysis, etc.).
This application is for a 6-month student role from January - June 2025. Resume review begins in October 2024.
Our team is Computational Biology. The aim of our team is to perform high quality computational analysis on genomics/transcriptomics datasets, standardize NGS data analysis pipelines, and work on cutting edge AI/ML tools and technologies. We collaborate closely with all discovery teams to perform computational analysis of NGS datasets. These includes analysis of RNAseq, snRNAseq, spatial transcriptomics data to perform target identification, engagement and assessment.
Position Description
Detailed description of role including but not limited to:
Leverage cutting-edge machine learning models to improve 1.single cell RNA-seq analysis, 2. Image segmentation for spatial transcriptomics
Implement machine learning algorithms and develop computational tools/packages
Attend scheduled team bi-weekly meetings
Give bi-weekly updates of progress.
Example projects may include:
Utilizing computer vision and LLM models to enhance segmentation in spatial transcriptomics data and refine cell calling across diverse spatial transcriptomics platforms and tissue types
Developing and maintaining computational tools (gpu computing, scRNA-seq analysis, etc.).
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