- Application deadline
- Not stated: check the official notice
- Stipend / salary
- As per norms
- Qualifications mentioned
- PhD, Master's (MSc/MTech/MPharm), Bachelor's (BSc/BTech/BPharm)
- Posted
- 25 Jun 2026 · Government
About this position
_______________________________________________________________________________________________________ Advertisement No.: NIBMG/ ACAD/PROJECT RECT./2026-27/094 Date:25 - 06 -2026 Position available: Online Interview BRIC-National Institute of Biomedical Genomics (NIBMG) Kalyani is an Institute of Government of India, Ministry of Science & Technology, Department of Biotechnology. The Institute has created the necessary physical infrastructure and critical platform technologies to support research, translation and training in biomedical genomics. Applications are invited for the position of Project Linked Person (Senior Project Associate) for extramural project to be executed by the BRIC-National institute of Biomedical Genomics, Kalyani. Selected applicants will be appointed initially for one year. The tenure of appointment may be extended depending on performance and availability of funds. Project Name: An Integrative-genomics framework to identify core genes casually driving complex trait risk. Principal Investigator’s name: Dr. Samsiddhi Bhattacharjee Research Description: Genome-wide Association Studies (GWAS) have discovered thousands of genomic variants associated with complex traits in humans but the downstream mechanisms and driver genes are unknown. The ‘omni-genic model’ for genomic architecture of complex traits posits that there are a few trait-specific ‘core’ i.e. driver genes. Genomic variants indirectly influence the core genes through perturbation of the gene regulatory network. Identifying the core genes is crucial for understanding diseases, their management and therapeutic targets. Through this study, we aim to build an framework that will enable identification of ‘core’ genes that are causal drivers of specific complex traits (e.g. diabetes or heart disease or schizophrenia), some of which will be validated experimentally. The framework will be applied to integrate large- scale public GWAS datasets, with those from eQTL, transcriptome and epigenome studies. The study will utilize bioinformatics database annotations, AI/ML algorithms, statistical and computation techniques for integrative genomics. The algorithms will be implemented for general use and disseminated publicly. Place of Work: BRIC-NIBMG, Kalyani