We have multiple positions available in various Bioinformatics, Computational Biology, Statistical Genetics and Data Analytics.
Statistical Genetics and Computational Genomics
We seek outstanding researchers with the potential to establish impactful, independent and creative Precision Medicine research programs at the GIS, and play a leadership role in Singapore’s National Precision Medicine (NPM) Program.
The Statistical Genetics faculty position requires expertise in population genetics and next-gen sequencing (NGS), as well as the ability to develop new statistical and computational methods relevant to human genetics. The candidate will be expected to lead the c-BIG team analyzing whole-genome sequencing data from over 10,000 Singaporeans, as part of the NPM program. The candidate will also be expected to work closely with clinicians to examine the genetic basis of various human diseases. Experience in integrating data from electronic health records (EHR) or electronic medical records (EMR) is a plus.
The Computational Genomics faculty positions require expertise in areas such as machine learning, algorithm development, NGS data analysis, disease omics (cancer, metabolic diseases including diabetes, heart disease, infectious disease, neurological disease), whole genome sequencing, transcriptomics, aging and image analysis. Candidates will be expected to build a strong, interdisciplinary research program and work closely with the clinical community and industry to lead projects that have a positive social impact.
All faculty candidates are expected to have demonstrated exceptional originality and excellence in their previous work. We have immediate openings and expect to fill several positions in the next two years. Applicants* should submit a CV, a statement of research interests and the names of three references by email (with job code “cBIG-Faculty” in the subject line) to: firstname.lastname@example.org
Group of Andreas Wilm (Research Pipeline Development Group; Genome Institute of Singapore)
We are a core group within GIS that offers scientific research computing services. We handle all next-generation sequencing data produced in GIS and provide primary as well as secondary analysis at scale.
In the context of the National Precision Medicine Programme (SG10K), we are seeking a motivated Postdoctoral Fellow in Computational Genomics, with experience in handling and interpreting large amounts of genomics data. Specifically, we want to explore the use of modern population genomics tools like GATK4 on Spark or Graph Genome implementations, which allow to analyse population scale genomics data efficiently.
GIS website: SRC
To apply, please email your CV and names of references to: Andreas Wilm
Spatial Transcriptomics And Single Cell Oncology Program
PhD Students / Postdoctoral Fellows (Wet Lab and Computational Tracks)
We are a large, well-supported multidisciplinary program that aims to introduce new paradigms for cancer biology and diagnostics using spatial and non-spatial omics technologies.
Our team comprises oncologists (lead: Iain Tan
), computational biologists (lead: Shyam Prabhakar
), biotechnologists (lead: Kok Hao Chen
), and pathologists (lead: Tony Lim
) with a track record of combining cutting-edge computational and experimental approaches to infer disease mechanisms and develop clinical applications (Chen et al., Science 2015; Li et al., Nat Genet 2017; Sun et al., Cell 2016; Fukawa et al., Nat Med 2016; del Rosario et al., Nat Methods 2015; Kumar et al., Nat Biotechnol 2013; Lancet Oncol 2012).
Wet Lab Track
Assay Development, Cancer Markers and Mechanisms
Successful candidates will have the opportunity to lead experimental design and execution for a spatial transcriptomics study looking at DNA and RNA changes in a variety of human cancers at subcellular resolution.
- Expertise in cancer biology, immunology, genomics or related fields.
- Skilled in molecular and cellular assays.
- Strong publication record (postdoctoral candidates).
- Team player and strong communication skills (oral and written).
- The ability to work closely with clinicians and computational biologists.
Machine Learning and Mathematical Analysis of Spatial Transcriptomics Data
Successful candidates will develop and apply algorithms for the analysis of large-scale cancer data. This will be a unique opportunity to lead computational analysis of new types of data in the nascent field of spatial transcriptomics.
- Strong programming skills.
- Expertise in mathematics, computer science, statistics, engineering, machine learning, signal processing, computational genomics, or a related field.
- General quantitative intuition.
- Strong publication record (postdoctoral candidates).
- Strong communication skills.
- The ability to work closely with clinicians and experimental biologists
To apply, please email your CV and names of references to:
Group of Shyam Prabhakar (Single-cell Data Analytics and Epigenomics)
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 analysis projects in the areas of autism, depression and autoimmune diseases such as Type 1 diabetes. We have 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). The focus of this call for applications is on cohort-scale epigenomic profiling and single cell omics to understand mechanisms of psychiatric and autoimmune diseases, working in close collaboration with wet-lab counterparts.
Qualifications: Candidates for all levels should have a bachelor’s degree in a quantitative field, strong mathematical intuition, a strong publication record (postdoctoral candidates), strong communication skills and programming skills, the ability to design new algorithms and the ability to work closely with clinicians and experimental biologists. Expertise is required in at least some of the following areas: mathematics, statistics, machine learning, signal processing, computational genomics.
GIS website: Shyam Prabhakar
To apply, please email your CV and names of references to: email@example.com
GROUP OF PAVITRA KRISHNASWAMY (I2R – PRECISION MEDECINE)
A research scientist position is available immediately at the Institute for Infocomm Research (I2R), A*STAR, Singapore. The project focuses on development of machine learning, deep learning and artificial intelligence algorithms for applications in precision medicine.
Specific research themes include:
- Multimodal data analytics on heterogeneous healthcare datasets (genomics, EMR, imaging, lifestyle) to predict treatment response and/or patient outcomes
- Methods to incorporate domain knowledge with machine learning and deep learning approaches for biomedical data analysis
- Interpretable machine learning and deep learning approaches for biomarker identification, knowledge discovery and clinical applications
The position entails working in a multi-disciplinary machine learning and deep learning team in close collaboration with bioinformatics experts, biologists, clinicians, as well as other leading academic and industry partners on impactful projects that have the potential to transform patient-care and deliver improved health outcomes. Appointments will be based in Singapore for 3 years duration.
A research engineer position is available immediately at the Institute for Infocomm Research (I2R), A*STAR, Singapore. The project focuses on development of machine learning, deep learning and artificial intelligence algorithms for applications in precision medicine.
Candidates should have demonstrated interests or experience in one or more of the following:
- Analysis of large scale heterogeneous biomedical datastreams (genomics, EMR, imaging, lifestyle)
- Projects involving biomarker identification, knowledge discovery, predictive analytics for patient outcomes,
and/or clinical application
- R&D for advanced algorithms
Core responsibilities include preprocessing of raw disparate biomedical datasets, development of automated knowledge extraction and feature engineering pipelines, and design of pilot studies/demos. The position entails working in a multi-disciplinary machine learning and deep learning team in close collaboration with bioinformatics experts, biologists, clinicians, as well as other leading academic and industry partners on impactful projects that have the potential to transform patient-care and deliver improved health outcomes. Appointments will be based in Singapore for 2 years duration.
to get details on both positions.
To apply, please email your CV and names of references to: Pavitra Krishnaswamy
Group of Niranjan Nagarajan (Sequence Analysis and Metagenomics)
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:
- Computational PhD
- Postdoctoral Fellow (Skin Microbiome)
- Postdoctoral Fellow (RNA Structural Genomics)
- Bioinformatics Engineer
Post-doctoral Fellowship in RNA Structural Genomics
SPLASH Graphical Abstract (Aw et al, 2016)
PARTE Graphical Abstract (Wan et al, 2012)
We are seeking highly qualified computational scientists with expertise/interest in RNA structure and biology. The Nagarajan Lab, in collaboration with the Wan Lab at the Genome Institute of Singapore are looking for applicants to direct collaborative projects involving the development of new algorithms and the analysis of novel high-throughput, genome-scale RNA structure datasets. Successful candidates will work in a highly inter-disciplinary environment to apply cutting edge techniques in computational and experimental genomics for achieving breakthrough discoveries that significantly advance human health and well-being.
RNA structure is fundamental to its function in almost every cellular process and for understanding disease biology. We have developed new approaches to probe RNA secondary and tertiary structure genome-wide [1-5] and are applying these for a range of applications including (i) identification of new targets for developing antibiotics against extremely drug-resistant pathogens (ii) understanding host-microbial interactions in diseases and (iii) identifying structural switches that control gene regulation during stem-cell differentiation and oncogenesis. We are seeking exceptional researchers who will join us in leveraging this unique opportunity for significant basic and translational advances in RNA genomics.
E-mail cover letter, curriculum vitae, and contact details of at least three referees to Niranjan Nagarajan (firstname.lastname@example.org) and Wan Yue (email@example.com)
- PhD in Computational Biology/Computer Science/Applied Mathematics/Genomics
- Strong analytical and programming skills (C/C++, Perl/Python, UNIX environment). Candidates with significant experience in RNA structure prediction will be strongly preferred.
1. Tapsin S, Sun M, Shen Y, Zhang H, Lim XN, Susanto TT, Yang SL, Zeng GS, Lee J, Lezhava A, Ang EL, Zhang LH, Wang Y, Zhao H, Nagarajan N#, Wan Y#. Genome-wide identification of nature RNA aptamers in prokaryotes and eukaryotes. Nat. Communications. 2018 Mar 29;9(1):1289
2. Aw JG, Shen Y, Wilm A, Sun M, Lim XN, Boon KL, Tapsin S, Chan YS, Tan CP, Sim AY, Zhang T, Susanto TT, Fu Z, Nagarajan N#, Wan Y# "In Vivo Mapping of Eukaryotic RNA Interactomes Reveals Principles of Higher-Order Organization and Regulation." Mol Cell 2016 May 19 ; 62(4) : 603-17 Epub 2016 May 12
3. Wan Y, Qu K, Ouyang Z, Kertesz M, Li J, Tibshirani R, Makino DL, Nutter RC, Segal E, Chang HY "Genome-wide measurement of RNA folding energies." Mol Cell 2012 Oct 26 ; 48(2) : 169-81 Epub 2012 Sep 13
4. Wan Y.*, Qu K.*, Zhang QF, Manor O, Ouyang Z, Zhang J, Snyder MP, Segal E and Chang HY "Landscape and variation of RNA secondary structure across the human transcriptome." Nature 2014 ; 505, 706–709
5. Kertesz M*, Wan Y*, Mazor E, Rinn JL, Nutter RC, Chang HY, Segal E "Genome-wide measurement of RNA secondary structure in yeast." Nature 2010 Sep 2 ; 467(7311) : 103-7
GIS website: Niranjan Nagarajan
To apply, please email your CV and names of references to: firstname.lastname@example.org
Group of Anders Jacobsen Skanderup (Computational Cancer Genomics)
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).
- Postdoctoral Fellow (Computational Cancer Genomics)
- Bioinformatics specialist / Data Analyst (Computational Cancer Genomics)
Lab website: Skanderup lab
Group of Jonathan Goeke (Transcriptomics, Cancer Genomics, Machine Learning)
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/
GIS website: Jonathan GÖKE
Interested candidates can contact Jonathan Göke (email@example.com). Additional information about the Goeke lab: