Genome Institute of Singapore: The Genome Institute of Singapore (GIS) is a uniquely international and collaborative research environment set in the modern and diverse Biopolis campus. Singapore is a vibrant, cosmopolitan city-state located just 100 km north of equator. Enjoy sunshine throughout the year, lush tropical surroundings, and a very modern and developed infrastructure. Singapore is very welcoming towards new skilled residents, and provides excellent opportunities for work, family, and leisure time.
Dept. of Computational and Systems Biology: The department of Computational and Systems Biology (CSB) forms an integral part of GIS as data analysis hub and center of excellent computational research. CSB houses 9 dedicated computational research groups that bring together a rich expertise in computer science, statistics, and bioinformatics. The computational teams are supported by state of the art sequencing facilities and high performance computing infrastructure that enables cutting-edge genomics research. Researchers have the opportunity to directly interact with experimental and clinical research labs at GIS to facilitate truly interdisciplinary and translational research projects.
PhD Scholarships and internships: All research groups in Computational and Systems Biology at GIS offer PhD scholarships (currently for August 2016 intake) and internships (6-months duration). Please contact the group leaders directly if you are interested, and see here for more information.
RESEARCH GROUPS AND OPEN POSITIONS
Group of Niranjan Nagarajan (Sequence Analysis and Metagenomics)
Lab website: http://csb5.github.io/
GIS website: Niranjan Nagarajan
We study the role of microbial communities in human health and disease using integrative omics approaches and systems models, combining wet and dry-lab expertise. We also frequently develop novel statistical and combinatorial algorithms for the assembly and analysis of high-throughput sequencing data (see Gao et al. 2011, Wilm et al. 2012, Bertrand et al. 2014). Current areas of focus include a) modelling pertubations in the gut microbiome in response to antibiotics and colonization by pathogens b) studying the role of the microbiome in skin diseases, and c) developing algorithms for nanopore sequencing data and RNA structure analysis. Open positions in the lab include:
To apply, please email your CV and names of references to: firstname.lastname@example.org
Group of Shyam Prabhakar (Single-cell Data Analytics and Epigenomics)
GIS website: Shyam Prabhakar
The Prabhakar lab at the Genome Institute of Singapore (GIS) is looking for creative and highly motivated PhD students and Postdoctoral Fellows to drive epigenomics and single-cell genomics projects in the areas of cancer and immune aging. We are a well-supported lab (Singapore Agency for Science, Technology and Research; US National Institutes of Health; Singapore National Research Foundation; multiple industry partners) with strong links to clinicians and a track record of combining cutting-edge experimental and computational approaches to infer gene regulatory mechanisms in development and disease (Kumar et al., Nat Biotechnol 2013; del Rosario et al., Nat Methods 2015; Sengupta et al., biorXiv 2016; Cima et al., Sci Transl Med 2016, Sun et al., Cell 2016). In addition to answering basic questions regarding molecular and cellular phenotypes, our current focus is on analyzing cohort-scale epigenomics and single-cell omics data to identify novel cell types, biomarkers and druggable pathways. Open positions in the lab are in epigenomics and single cell analysis of multiple diseases, including lung and colon cancer, tuberculosis, autoimmune diseases and autism:
- Computational Genomics PhD
- Computational Genomics Postdoctoral Fellow
Qualifications: Candidates should have training in a quantitative field, a strong publication record (postdoctoral candidates), strong writing skills, the ability to design new methods and the ability to work closely with clinicians and experimental biologists. Expertise is required in at least some of the following: mathematics, statistics, machine learning, signal processing, next-gen sequence analysis.
To apply, please email your CV and names of references to: email@example.com
Group of Anders Jacobsen Skanderup (Computational Cancer Genomics)
Lab website: Skanderup lab
We develop and apply computational and statistical approaches for cancer research that take advantage of large-scale genomic, molecular, and clinical data. We are particularly interested developing computational approaches to decipher the influence of non-coding elements and transcripts in cancer using rich tumor profiles and massive DNA sequence data (see Weinhold, Jacobsen et al. 2014, Nature Genetics; Jacobsen et al. 2013, Nature Structural & Molecular Biology).
Group of Chaolong Wang (Statistical and Population Genetics)
Lab website: Wang lab
As a computational group, we collaborate closely with biologists and clinicians to study population genetics and various human genetic diseases. We develop and distribute novel statistical and computational methods to address new challenges arise from large-scale human genetics and genomics data when there is no off-the-shelf tool available. More information about our research can be found on our website.
Group of Jonathan Goeke (Transcriptomics, Cancer Genomics, Machine Learning)
Lab website: Goeke lab (www.jglab.org)
GIS website: Jonathan GÖKE (https://www.a-star.edu.sg/gis/our-people/investigator-details.aspx?source=faculty_member&user_id=160)
We investigate transcript diversity, alternative splicing, gene regulation, and epigenetics to identify key elements and mechanisms that regulate cellular identity. Our main focus is the large scale cancer data analysis. We are closely collaborating with wet labs to translate computational models to cellular phenotypes.
The Goeke lab is currently searching for PhD students (starting in August 2018) and postdoctoral fellows with a background in bioinformatics, computational biology, computer science or statistics.
The following projects are available for PhD students starting 2018:
- Algorithms for long read transcript quantification
- Statistical modeling of uncertainty using large-scale transcriptomics data
- Machine learning approaches to predict clinical data from genomics data
We are looking for a postdoctoral fellow to work on algorithms for long read transcript quantification. Details about the postdoc position can be found online (https://jglab.org/postdoc-positions/).
Interested candidates can contact Jonathan Göke (firstname.lastname@example.org). Additional information about the Goeke lab: https://www.a-star.edu.sg/gis/our-people/investigator-details.aspx?source=faculty_member&user_id=160