We are happy to announce an student internship opportunity as part of KAUST VSRP programme. The internship, slated for July/August 2018, is available to undergraduate/Postgraduate students from computer science/bioinformatics/computational biology. Students in computational biology or any related areas are welcome to apply. A stipend of 1200-1500USD/month and economy class airfare,accomodation will be provided to the selected intern. For further informations, please contact rajkumarravi2017new@gmail.com
Computational epigenetics is an interesting interdisciplinary area of research that involves development of computational methods to analyze large scale epigenomic data. Advent of Large-scale high-throughput experiments have resulted in generating huge amount of binding signal data (enrichment profile) for factors associating with genomes. Analysis of this kind of big dataset involves development of methods and tools to characterize these binding peak signals (epigenetically modified regions). Our project focuses on combinatorial approach to define boundaries of these epigenetically modified regions in complete genomes and to classify and characterize these regions into segments or domains. A student project work on this project will entail developing an algorithm to find statistically significant domains of ChIP-enriched signals and to develop innovative computational strategies involving machine-learning approaches for the integrative analysis of the above said high-throughput multi-omics data. The outcome of this project will be a user-friendly software solution for epigenomic data analysis to the bioinformatics community.
Responsibilities:
- Knowledge of programming in C++ in *nix environment.
- Develop (or assist/apply) computational method for segmentation of genomes using a combination of epigenomic datasets.
- Application of machine learning techniques to predict 3D architecture of the segments of epigenomes.
- Maintain good log(CVS), Submit writing and present results.
Qualifications:
Masters/Bachelors in Engineering/Computer science/Bioinformatics or any other related areas.
