2024 Summer Internship - Computational Researcher

Genentech

Jan 29, 2024

Applications are closed

Internship
Full-time
Summer Internship
Research & Development
San Francisco

Requirements

Must be pursuing a PhD focused in Bioinformatics, Biostatistics, Computational biology, Computer science, or Bioengineering
Enrolled within an accredited university
Required Skills:
Demonstrated proficiency and deep understanding in Bioinformatics, Biostatistics, Computational biology, Computer science, or Bioengineering;
Experienced in Spatial Transcriptomics and Single-Cell RNA-Seq Analysis: A solid track record of working with spatial transcriptomics and single-cell RNA sequencing data, showcasing a thorough understanding of these cutting-edge techniques
Preferred Skills:
Advanced Proficiency in Python and R: Skilled in programming with Python and R, including extensive experience with specialized packages such as Seurat, Scanpy, and scVI-tools, essential for omics data analysis;
Experience with Imaging Analysis and Deep Learning Frameworks (Preferred): Familiarity with imaging analysis (OpenCV, scikit-image, etc.) and deep learning frameworks (PyTorch, TensorFlow, Keras, etc.) is an advantageous addition, enhancing the capacity to handle image and high-dimensional datasets.
Excellent communication, collaboration, and interpersonal skills.
Complements our culture and the standards that guide our daily behavior & decisions: Integrity, Courage, and Passion.

Responsibilities

Analyzing Spatial Transcriptomics with Multiple Platforms: The intern will analyze spatial transcriptomics data using various platforms such as 10X Visium, Xenium, and NanoString CosMx. This will also include the analysis of single-cell sequencing data.
Benchmarking Different Cell Annotation/Label Transfer Approaches: A significant responsibility will involve benchmarking various cell annotation and label transfer methods (including single-cell reference-based approach and reference-free approach). This task will require the intern to assess the efficiency and accuracy of different approaches, comparing their performance in various scenarios. The intern will need to have a good understanding of computational biology and statistical methods to effectively evaluate these approaches.
Providing a Systematic Recommendation Workflow: Lastly, the intern will be expected to develop and provide a systematic workflow for recommendation purposes. This workflow should integrate the insights gained from the analysis of spatial transcriptomics and the benchmarking of cell annotation methods. It should be designed in a way that is user-friendly and can be easily adapted for different research needs. The intern will need to have strong skills in data integration and workflow development, along with the ability to clearly communicate their recommendations.

Genentech

Science & Healthcare

Industry

10,001+

Employees

1976

Founded Year

Mission & Purpose

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