锦鲤求职岗位描述
岗位职责
Introduction:
Leveraging single-molecule long-read sequencing to construct population-scale genomic and multi-omics reference atlases, systematically resolving human genetic and epigenetic diversity to decode population evolution, disease susceptibility, and risk stratification for precision prevention.Particular emphasis is placed on structural variants (SVs), tandem repeats (TRs), phased haplotypes, and medically important complex loci (e.g., MHC/HLA), with systematic cross-platform benchmarking and quality assessment.
The subtrack also integrates genetic variants with haplotype-resolved DNA methylation, transcriptomic, proteomic, metabolomic, and single-cell data to investigate molecular regulation (e.g., meQTL, eQTL, and pQTL), prioritize causal variants, and elucidate human diversity and adaptation.
This subtrack supplies the methodological core of that mission: developing and benchmarking new computational methods for long-read structural variation detection, de novo assembly and error correction toward T2T-level reconstruction of complex regions, and pangenome graph construction, genotyping, and downstream application — turning raw long-read data into reliable, reusable reference resources across DNBSeq, CycloneSeq, and hybrid platforms.
Emerging directions include AI-assisted genomics, genome foundation models for variant-effect prediction, and scalable algorithms for population-level analysis. Students may pursue methods-driven or biological discovery projects with an emphasis on reproducibility and translational relevance.
Affiliated Branch(es):
Guangdong–Hong Kong–Macao Branch (Shenzhen)
Hangzhou Branch (Hangzhou)
岗位要求
(1)Preferred Background:
Bioinformatics, Biostatistics, Computational Biology, Genomics, Molecular Biology, Epigenetics, Epidemiology, Statistical Genetics, Biotechnology, and related disciplines, including Computer Science, Applied Mathematics, and Machine Learning.
(2)Core Technical Skills:
Wet-lab: DNA extraction & QC, epigenetic assays. Familiarity with long-read library preparation and sequencing quality control is an advantage.
Genomics & Multi-omics Analysis: SV/SNV calling, phasing, de novo assembly & polishing, Pangenome construction, (post-) GWAS analysis，EWAS，QTL，multi-omics integration. Additional relevant experience includes TR discovery/genotyping, graph-based alignment, haplotype-resolved methylation, allele-specific analysis, and functional fine-mapping.
Computing: R/Python，Pipeline development& management. Familiarity with Linux/HPC, Git, Snakemake/Nextflow, containers, and large-scale genomic data processing is desirable.
(3)Modeling Competency:
Regression analysis, survival analysis, causal inference (MR), and risk prediction (MRS/PRS/epigenetic clock) with rigorous validation (ΔAUC, calibration). Implemented in R/Python. Additional strengths include mixed models, population-structure adjustment, statistical fine-mapping/colocalization, batch-effect assessment, and validation across cohorts or platforms.
Knowledge of machine learning or deep learning for genomic sequence analysis is welcomed but is not a prerequisite.
(4)Preferred Qualifications:
Hands-on bioinformatics research experience (not limited to coursework)
Basic Mandarin Chinese skills (helpful but not essential for applicants with strong English communication skills).
Evidence of research initiative, such as independent coding projects, reproducible analysis pipelines, or scientific presentations.
(5)Personal Traits:
Self-motivated with strong self-learning ability and persistence in solving technical challenges independently
Collaborative communicator with ability to work effectively across interdisciplinary teams
Strong time management, and high work efficiency.
Scientific curiosity, critical thinking, and a commitment to data integrity, reproducibility, and clear documentation.