Recipient of the esteemed International Translational Neuroscience Award, Dr. Yan, a pioneer in Brain Tumor research, joins IMCB to establish the NeuroPhenomics Lab. This lab marks a new chapter in our quest for profound biological insights and their translation into biomarkers and therapies.
The lab"s mission revolves around a profound exploration of brain tumor biology, particularly focusing on key driver mutations like IDH1 and IDH2. These mutations, significant in the development of aggressive malignant gliomas, are pivotal in altering chromatin and genome methylation, thus influencing tumor evolution. The lab extends this research by identifying other genetic alterations with potential clinical relevance.
Dedicated to exploring new frontiers, the NeuroPhenomics Lab integrates advanced techniques in genetics, epigenetics, and metabolomics at both single-cell and spatial levels. This approach allows for a detailed examination of tumor cell diversity and their interactions within the microenvironment. The lab also investigates neurometabolism, emphasizing the TCA cycle and IDH enzymes, and studies how metabolites affect the brain and tumor progression.
Aiming to bridge research and clinical application, the lab"s multi-dimensional strategy includes iPSCs, patient-derived organoids, transgenic models, and advanced imaging technologies. This positions the NeuroPhenomics Lab at the forefront of developing transformative therapies in neurooncology and neurometabolism.
Job Opportunity:
Join the NeuroPhenomics Lab in its endeavor to redefine brain tumor genomics. We seek individuals with a strong background in Computer Science or Bioinformatics, particularly in data analysis, to contribute to our innovative research.
Key Qualifications:
- PhD in Bioinformatics, Computer Science, Biostatistics, or a related field with a research focus.
- Individuals planning to pursue PhD training (at NUS or NTU) are also welcome to apply.
- Expertise in bioinformatics and next-generation sequencing data analysis.
- Experience with Single-cell sequencing and Spatial-omics data analysis is a plus.
- Familiarity with developing AI models for visual recognition of imaging data.
- Experience in applying research to train Large Language Models (LLM) is advantageous.
- Preferred experience in Computer-Aided Drug Discovery.
- Exceptional analytical, technical, and problem-solving skills.
- Ability to work independently and thrive in a dynamic team environment.
