Postdoctoral fellow in Single-cell Genomics

The Laboratory for Genome Information Analyses (led by Dr. Chung-Chau Hon) in  Hiroshima University and RIKEN Center for Integrative Medical Sciences is seeking applicants for postdoc positions. The primary affiliation of this position is Hiroshima University, Graduate School of Integrated Sciences for Life. The successful candidate will be also affiliated to RIKEN Center for Integrative Medical Sciences, as a visiting researcher. Regular travels between Hiroshima University in Higashi-Hiroshima and RIKEN at Yokohama are expected.

Our mission is to understand the origins and functions of non-coding transcription in the human genome, with a focus on long non-coding RNAs and enhancer RNAs. We have been at the forefront of annotating these non-coding RNAs using large-scale in-house transcriptomic datasets (e.g. FANTOM consortium). We also integrate our in-house datasets with public epigenomic and genetic (e.g. GWAS and eQTL) datasets to gain insights into the roles of non-coding RNAs in human diseases. Our team is primarily composed of bioinformatic analysts specialized in transcriptomics, but we are working closely with experimental biologists to provide bioinformatics support on technology development (e.g. single-cell transcriptomics), as well as participating other collaborative projects (e.g. Human Cell Atlas). Our research focuses on the following areas: 1) Integrating genetic data with population-scale single-cell and bulk transcriptomic/epigenomic data (i.e. GWAS and eQTL) to gain insights into human diseases, in particular autoimmune diseases; 2) In vitro modeling neurodegeneration using patient derived iPSC at population-scale; 3) Cohort scale single-cell profiling of tumor transcriptome and somatic mutation; For further details, please refer to the publications of Dr. Chung-Chau Hon on google scholar. 

The successful candidate will be responsible for analyzing the population-scale single-cell transcriptomic and epigenomic datasets generated from neurodegeneration, autoimmune diseases or cancer cohorts. These analyses include, but not limit to, unsupervised cell clustering, cell-types annotation, single-cell eQTL, cell-type specific enrichment of GWAS signal, gene co-expression network and beyond. These projects highlight the integration of genetic data with single-cell RNA-Seq/ATAC-Seq data to identify active functional non-coding elements across cell-types, aiming 1) to elucidate the roles of non-coding elements in diseases, and 2) to facilitate the interpretation of causality of non-coding variants in human disease predisposition. The candidate is expected to apply the most cutting-edge computational and statistical methods for single-cell omics and genetics to construct personalized (based on genetic variations) gene regulatory networks, which would ultimately provide guidance for precision medicine in the future. Candidates with interests in single-cell omics, human genetics, neurodegeneration and autoimmune diseases are encouraged to apply.

[Qualifications]

1.A doctoral degree in a field related to the [Job Description].

2.Should have an experience and knowledge in bioinformatics.

3.Should have solid programming skills (Perl, Python and/or R) and acquaintance with Unix/Linux.

4.Should be familiar with genomics technologies, in particular those relevant to applications to next generation sequencing (e.g. RNA-seq, ChIP-seq, CAGE, etc.).

5.Should have an experience and knowledge in analyses of next-generation sequencing transcriptomic and epigenomic datasets.

6.Additional experience and knowledge in machine learning and artificial intelligence are meritorious, but not required.

7.Should be highly motivated with competence in running projects independently.

8.Should have excellent communication skills and be able to engage in research in a coordinated manner in order to complete the assigned tasks with the members of mixed nationalities.

9.Comfortable with English as a professional language for scientific discussion and publication (Japanese language skills are NOT required)