Khalifa University is seeking a dedicated and skilled Postdoctoral Research Fellow to contribute to an innovative project focused on advancing cancer diagnostics through the discovery and validation of novel biomarkers for early detection. The project centers on the analysis of alternative splicing (AS) processes that contribute to tumor development. The successful candidate will collaborate with a multidisciplinary team, developing platform-independent bioinformatics tools and algorithms for comprehensive AS analysis and classification, contributing to enhanced cancer diagnostics.
Khalifa University is ranked 181st in the QS World University Rankings 2023, with a range of research and academic programs designed to address the entire range of strategic, scientific and industrial challenges facing our rapidly evolving world. Its world-class faculty and state-of-the-art research facilities provide an unparalleled learning experience to students from the UAE and around the world.
Key Responsibilities:
• Algorithm and Tool Development:
o Develop and refine computational algorithms to analyze isoform-level data, incorporating advanced statistical and machine learning methods.
o Construct classification and validation models to predict cancer subtypes, addressing issues such as batch effect mitigation and data discretization.
o Design and implement the PIGExClass algorithm for clustering based on isoform expression patterns.
• Data Preprocessing and Analysis:
o Conduct preprocessing and normalization of multi-omics datasets, including RNA-seq data transformation.
o Apply variance-stabilizing transformations and dimensionality reduction techniques to ensure data integrity and compatibility across different platforms.
• Statistical Modeling and Batch Effect Removal:
o Develop robust statistical models for cross-platform AS analysis, incorporating supervised discretization to enhance data reliability.
• Clustering and Survival Analysis:
o Utilize consensus clustering methods and Kaplan-Meier survival analysis to assess the prognostic relevance of identified cancer subtypes.
• Pipeline Development and Web Interface Design:
o Integrate developed algorithms into a user-friendly bioinformatics pipeline, accessible to researchers
Essential Criteria:
• Ph.D. in Bioinformatics, Computational Biology, Data Science, or a related field.
• Strong programming skills in R, Python, or equivalent languages.
• Extensive experience with high-throughput sequencing data analysis, especially RNA-seq.
• Background in statistical modeling, machine learning, or data mining applied to large biological datasets.
• Familiarity with NGS analyses tools and various machine learning algorithms.
Desired Criteria:
• Knowledge of alternative splicing mechanisms and cancer genomics.
• Experience in developing bioinformatics tools and analytical pipelines for clinical applications.
• Prior experience in high-performance computing environments and collaborative, interdisciplinary projects.