Two PhD scholarships in BioMedical Machine Learning Lab at UNSW SYDNEY

Title: Deep-Learning techniques to analyse chromosome interactions at single-cell level

Abstract: The 3D genome organisation regulates gene expression by bringing distal regulatory elements, such as enhancers, to promoters in close spatial proximity. While many scientists have been working on cell-type specificity of gene regulation through transcriptomic sequencing, comprehensive investigation of cell-type specificity of 3D genome conformation patterns is still lacking. Recently, single-cell methods allow us to examine cell-type heterogeneity and profiling chromosome architecture at the single-cell level has been achieved using chromosome confirmation capture (Hi-C). However, unbiased and robust computational methods are urgently needed to study cell type-specific chromosome structural patterns and accurately identify local enhancer-promoter interactions at single-cell level.

The skills required for project(s): Scripting experience in Python, R, or MATLAB. Basic knowledge in biology.

What skills will you gain during your degree: Machine learning and data analytics techniques, Bioinformatics, Systems Biology.

Supervisory team: Drs Hamid Rokny

Title: Graph-Learning techniques to analyse electronic health records

Abstract: Despite the recent progress in deep learning and graph theory models, their applications in health decision-making systems have been very limited. Recent increment of electronic health records (EHR), however, has provided a great opportunity for the researchers to use state-of-the-art techniques in healthcare. Here we plan to develop/re-sue novel graph-based learning techniques with an application on EHR data.

The skills required for project(s): Scripting experience in Python, R, or MATLAB. Strong knowledge in deep learning and graph-learning based theories.

What skills will you gain during your degree: clinical decision-making systems, tools development, EHR

Supervisory team: Drs Hamid Rokny and Reza Argha.

The successful applicant

The ideal candidate must have an equivalent of First-Class Honours or Master degree. The candidate should be strongly experienced in computer science/mathematics or bioinformatics/biology, and should be highly motivated, and responsible. The applicants are expected to have computer programming experience in at least one of MATLAB, Python, R, or Java.

Note: Domestic applicants will also be provided up to $10K top-up per year, which supplements the base RTP.

How to Apply

Applications should be sent to h(dot)alinejad(at)unsw(dot)edu(dot)au. Your application should include:

CV; (ii) Copy of transcripts; (iii); (iv) English test result (if available).