Research Fellow

4 days ago

Singapore A*STAR Agency for Science, Technology and Research Full time

Relevant skillsets

1. Database and web portal development

2. Data integration / batch effect correction with disentangled representation learning

3. Cell type identification with transfer learning

4. Continual learning that continuously integrates new data to be published

**Theme 2**: Develop and deploy deep multimodal representation learning methods for the analysis and integration of single-cell multi-omics. Single-cell multi-omics simultaneously measures multiple-omes of a cell including its genome, transcriptome, epigenome, methylome, proteome, immune receptor repertoires and etc, providing a holistic view of individual cells and holds an unprecedented potential for dissecting cellular heterogeneity. Compared to single-omics, multi-omics is able to identify subtler differences between cells and reveal links across omes. When analysing such data, combining the multiple-omes to learn a low-dimensional discriminative representation for each cell is crucial. Specifically, joint embeddings of the multiple data modalities are essential for various down-stream analysis tasks such as visualization, clustering, trajectory inference, and batch integration. CJM lab builds AI models to learn each data modality by dedicated deep neural network and then jointly train them with multi-view learning to produce an unsupervised embedded deep representation of cells. With this, we seek to achieve higher resolution of cell type identification; discover new cell populations & their associated functions; uncover relationships across-omics and predict across modalities.

**Relevant skillsets**:
1. Multimodal AI / multi-view learning

**Theme 3**: Build human spatial omics atlas and construct cell-cell interactomes.

In current single cell analysis, cell-cell interactions are computationally predicted solely based on ligand and receptor expression and without spatial information. The emerging spatial omics technologies enable simultaneous measurement of gene/protein expression and cell locations, which is critical for characterizing cell-cell interactions. We first build human spatial omics atlas by integrating public datasets and in-house spatial omics data generated for human tissues. To analyse spatial omics for cell-cell interactions, we develop deep graph neural network models to predict which cell types are interacting via which ligand-receptor pairs.

**Relevant skillsets**:
1. Image processing and computer vision for processing the image data of spatial omics

2. Graph neural network

3. Social network analysis

**Jobs Requirements**:
PhD in any of computer science/engineering, mathematics, data science, computational biology, bioinformatics, statistics, or related fields
Strong background in AI, deep learning, machine learning, pattern recognition, data mining, image processing, database, statistics and etc is preferred
Experience/ability to develop new data analysis methods/databases
Programming skills using any of R/python/perl/shell script/C++/Matlab/Java or similar
Experience in analysis of single-cell omics data, mass/flow cytometry, or NGS (optional)
Good team player, with good interpersonal skills
Interest or background in biology or willing to learn
Initiative, self-driven and critical thinking
Ability to learn new technologies, willing to accept new challenges

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