MASTER’S AND PHD STUDENT POSITIONS

 

Master’s and PhD student positions are available in Biomedical Computing Group led by Professor Jagath Rajapakse (https://personal.ntu.edu.sg/asjagath/) at Nanyang Technological University, Singapore. Interested applicants should forward their CVs and academic transcripts to Professor Jagath Rajapakse at asjagath@ntu.edu.sg.

 

Topics currently available for research:

  

Functional and structural connectome analysis:

1.     Encoding the connectome: Encoding involves learning representations from functional MRI and diffusion tensor imaging (DTI) scans to disease classification and predict clinical measures and drug response. Deep learning techniques are most popular though such techniques require large neuroimaging datasets. In this project, we will develop graph neural network approaches to preserve the graphical structure of the connectome and unsupervised learning technique to overcome the paucity of neuroimaging data.

2.     Decoding the connectome: By decoding the connectome we identify brain regions and connections that are implicated in brain disease. This requires selection of features and interpreting functioning therein of deep neural networks. In this project, we will develop deep neural networks such as graph neural networks and identifying salient nodes therein detecting brain disease.

3.     Combining neuroimaging data with multi-omics data: To investigate neurodegenerative disease, both neuroimaging and multi-omics data are increasingly being gathered. However, techniques to integrate neuroimaging and omics data are lacking and we will investigate deep learning approaches that integrate multi-omics and neuroimaging data to detect brain disease and identify potential drug targets.

 

Multi-omics data analysis:

1.     Drug response prediction from multi-omics data: We want to develop methods to predict drug response from multi-omics (genomics, proteomics, transcriptomics, and epigenomics) data gathered from patients. We will first develop deep neural network approaches for drug response prediction from multi-omics data gathered from cell lines. Thereafter, we will develop translational deep learning methods to translate such models on patient samples.

2.     Predicting drug-target interactions from drug response data: We will develop deep neural networks approaches to predict drug response by combining both drug chemical data and multi-omics data. We will use graph neural network approaches with attention mechanisms and use salience backpropagation techniques to identify chemical features and omics features that are responsible and interacting with one another for creating a drug response.  

 

Explainable and Generative AI:

 

 

 

Notes to potential candidates:

·      NTU requires minimum GRE and TOEFL scores for admission to graduate programs

·      Students with excellent academic scores and prior research experience are eligible for scholarships

·      There are research assistantship positions for self-funding master’s and PhD students, which will cover most of their expenses

·      Visiting students (for at least for two semesters) are considered on a case-by-case basis

·      All interested students should send their academic transcripts and CV to Professor Jagath Rajapakse (asjagath@ntu.edu.sg).