Post Doctoral Fellow

Khalifa University of Science, Technology and Research

Responsibilities

1. Develop and refine computational algorithms to analyze isoform-level data, incorporating advanced statistical and machine learning methods.
2. Construct classification and validation models to predict cancer subtypes, addressing issues such as batch effect mitigation and data discretization.
3. Design and implement the PIGExClass algorithm for clustering based on isoform expression patterns.
4. Conduct preprocessing and normalization of multi-omics datasets, including RNA-seq data transformation.
5. Apply variance-stabilizing transformations and dimensionality reduction techniques to ensure data integrity and compatibility across different platforms.
6. Develop robust statistical models for cross-platform AS analysis, incorporating supervised discretization to enhance data reliability.
7. Utilize consensus clustering methods and Kaplan-Meier survival analysis to assess the prognostic relevance of identified cancer subtypes.
8. Integrate developed algorithms into a user-friendly bioinformatics pipeline, accessible to researchers and clinicians, for AS analysis.

Candidate Profile

1. Ph.D. in Bioinformatics, Computational Biology, Data Science, or a related field.
2. Strong programming skills in R, Python, or equivalent languages.
3. Extensive experience with high-throughput sequencing data analysis, especially RNA-seq.
4. Background in statistical modeling, machine learning, or data mining applied to large biological datasets.
5. Familiarity with tools like RSEM, Kallisto, PCA, and various clustering algorithms.

Desired Criteria

1. Knowledge of alternative splicing mechanisms and cancer genomics.
2. Experience in developing bioinformatics tools and analytical pipelines for clinical applications.
3. Prior experience in high-performance computing environments and collaborative, interdisciplinary projects.