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ML Engineer / Computational Genomics 

Qualifications

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  • Advanced degree (PhD preferred) in Computer Science, Bioinformatics, Computational Biology, or a related field.

  • Strong expertise in RNA-seq data analysis tools (e.g., DESeq2, edgeR, Seurat).-

  • Proficiency in machine learning frameworks (e.g., TensorFlow, PyTorch, Scikit-learn)

  • Solid programming skills in Python, R, or equivalent.-

  • Familiarity with biological systems and gene expression data.Preferred Skills:

  • Experience with dimensionality reduction (e.g., Autoencoders, PCA, t-SNE).- Knowledge of neural network architectures for biological data (CNNs, RNNs)

  • Background in signal processing or biological data analysis.- Expertise in multi-omics data integration.​​​

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About the Role:

  • We are seeking a talented and motivated Machine Learning Scientist to join a 6-month project focused on developing AI models for analyzing RNA-seq data.
    This role involves designing and implementing cutting-edge machine learning pipelines to uncover meaningful patterns in gene expression data and gain insights into biological processes under various stressors.
    Successful outcomes may lead to an extension of the project. We are hiring ASAP and encourage qualified candidates to apply promptly.
    Key Responsibilities:

  • Data Preparation and Preprocessing:

    • Process and normalize RNA-seq datasets, ensuring quality and compatibility for machine learning analysis.

    • Integrate relevant metadata and other contextual information into datasets.

  •  Model Development and Training:

  • Develop and train models, including CNNs, RNNs, and Autoencoders, for detecting patterns and reducing dimensionality in RNA-seq data.

    • Apply predictive models to identify critical features in RNA-seq data.

    • Implement SVM-based classifiers to differentiate biological states.

  • Analysis and Interpretation:

    • Analyze model outputs to identify significant gene expression changes and pathways.

    • Work with biological teams to validate and refine findings.

  • Computational Optimization:

    • Optimize models for large-scale RNA-seq datasets.

    • Ensure reproducibility and robust performance of all machine learning pipelines.

  • Collaboration and Communication:

    • Collaborate with biologists, bioinformaticians, and data scientists to align computational efforts with biological questions.

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How to Apply:
Please send your resume, cover letter, and a brief description of a relevant project you have worked on to jobs@x10e.com.

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