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  Ph.D. scholarship in Genomic Data Science: Developing novel genomic data science methods to support the selective breeding of species for aquaculture

   Seafood Genomics

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  Assoc Prof Maren Wellenreuther  Applications accepted all year round  Funded PhD Project (Students Worldwide)

About the Project

Ph.D. scholarship in Genomic Data Science

PhD project title: Developing novel genomic data science methods to support the selective breeding of species for aquaculture

Project Supervisors 

Associate Professor Maren Wellenreuther, Science Group Leader of Seafood Production, Plant and Food Research (PFR), Nelson, and School of Biological Sciences, University of Auckland, New Zealand 

Dr Linley Jesson, Science Group Leader of Data Science, Plant and Food Research (PFR), Auckland, New Zealand

Dr Yi Mei, Victoria University of Wellington, New Zealand

General background and project

Aquaculture is the fastest

growing food-production sector and New Zealand has the potential to develop a

range of locally grown finfish species to meet the increasing demand. New

genomics-based selective breeding programs are needed to help develop recently

domesticated fish species into premium products. Plant and Food Research (

is known worldwide for its innovative breeding and genomics research, and it is

leading the development of New Zealand seafood genomics.

Genomics Data Science is an interdisciplinary field that applies statistics and the tools of data science to decode the functional information hidden in DNA sequences, but this has rarely been applied to aquaculture. Data generated by these technologies is often termed multi-omics data and can include information on DNA, RNA, proteins, epigenetic modifications and metabolites, amongst others (Navarro, Mohsen et al. 2019). Methods to analyse these large and complex genomic datasets include statistical and machine learning, and a host of new algorithms that can reveal structure in data, perform classifications, reveal causal patterns and make predictions.

We are looking for a highly motivated PhD student with a background in Data Science that can be applied to omic data sets from new finfish species for aquaculture, such as whole genome data, transcriptome and epigenome data. These omic data sets need to be integrated with environmental and biological performance data of the same individuals to be able to reveal causal relationships between genotype, environment and fish performance. Ultimately, our goal is gain an improved insight into the genotype-phenotype map to inform the selection of individuals for aquaculture breeding programmes (Ruigrok, Xue et al. 2022).

Data analyses will be performed to e.g. develop prediction models to understand likely outcomes from multifactorial genetic, environmental and treatment data, or to understand trade-offs in fish breeding (trade-off between growth and sexual maturity), or to develop predictive models based on structural genomic variation (as done here: Ruigrok, Xue et al. 2022).

This PhD project will provide an excellent opportunity to learn the latest interdisciplinary technologies and integrate them to combine Data Science and fish genomics. The PhD student will gain experience working in academic, government and private sector institutions. They will be a member of a highly active and collaborative group of researchers, and help develop new technological approaches and applied-genomic tools.

The successful candidate will be a highly motivated researcher with a strong background in computational approaches in biology, computer science or engineering. This project is well-suited for a BSc or MSc trained in mathematics or computer science who would like to develop cutting edge collaborative data and software development skills in an applied field. The project is also accessible to a civil and environmental or other engineer with proven programming skills and software design experience or any engineer who has shown extraordinary academic achievement and has a strong interest in modelling and software. Data analysis of next generation sequencing data will be the main workload of the project. Therefore, advanced knowledge and experience of a scripting language (Python/Perl) is a requirement and an object-oriented programming language (such as java, C++, C#) a bonus. A proven ability and motivation to write research papers is a plus.

This position will be based

primarily in Nelson ( and comes with a three-year scholarship

that provides a stipend and university (domestic-level) fees.

Applicants should send a CV, a

statement of their research interests and a cover letter to Maren Wellenreuther

([Email Address Removed]). Candidate selection will be considered

until the position is filled. International students with strong credentials

are welcome and encouraged to apply. For more information about studying at VUW

and the entry requirements for the PhD program please see

Biological Sciences (4) Computer Science (8) Food Sciences (15)

Funding Notes

This is a fully funded scholarship available worldwide.


Literature cited
Navarro, F. C. P., H. Mohsen, C. Yan, S. Li, M. Gu, W. Meyerson and M. Gerstein (2019). "Genomics and data science: an application within an umbrella." Genome Biol 20(1): 109.
Ruigrok, M., B. Xue, A. Catanach, M. Zhang, L. Jesson, M. Davy and M. Wellenreuther (2022). "The Relative Power of Structural Genomic Variation versus SNPs in Explaining the Quantitative Trait Growth in the Marine Teleost Chrysophrys auratus." Genes 13(7): 1129.
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