I am looking for applicants for a funded PhD at Aarhus Uni, Denmark. The focus is developing new methods in human statistical genetics (e.g tools for finding causal variants, predicting phenotypes and understanding genetic architecture). The PhD would be suitable for someone with a background in maths, statistics, engineering, computer science or genetics.
Please note, the application deadline this Saturday (1st May).
For more details, see below and this link Method development in human statistical genetics
Method development in human statistical genetics https://phd.tech.au.dk/for-applicants/apply-here/may-2021/method-development-in-human-statistical-genetics
Qualifications and specific competences: The applicant must have an Bachelors degree and a MSC degree (or similar). As the project involves applied statistics, the applicant should have strong knowledge of statistics (e.g., a degree in mathematics, statistics, genetics or closely-related subjects).
phd.tech.au.dk
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If you have any questions, please email Este enderezo de correo está a ser protexido dos robots de correo lixo. Precisa activar o JavaScript para velo..
Thanks, Doug
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Applications are invited for a PhD fellowship/scholarship at Graduate School of Technical Sciences, Aarhus University, Denmark, within the Department of Quantitative Genetics and Genomics (QGG) PhD programme. The position is available from 1 August 2021 or later.
Title: Method development in human statistical genetics
Research area and project description:
The PhD will be supervised by Professor Doug Speed, and based at the Department of Quantitative Genetics and Genomics (QGG) at Aarhus University. Dr. Speed's research involves developing methods for better analysing data from genome-wide association studies, with a particular focus on improving our understanding of human complex traits (e.g., physical traits such as height and BMI, or common diseases such as schizophrenia and epilepsy). Dr. Speed has developed the software LDAK (www.ldak.org).
The aim of the PhD is to develop new methods with the following goals: to identify causal loci (i.e., find genetic variant that influence phenotypes); to construct prediction models (be able to predict an individuals phenotypes from their genetic information); or to understand genetic architecture (investigate the biological mechanisms underlying complex traits). These methods will then be applied to large-scale datasets (e.g., 100,000s of individuals from UK Biobank). Please see the LDAK website and the following three references for examples of previous methods:
MultiBLUP: improved SNP-based prediction for complex traits (2015) PMID: 24963154 44
Reevaluation of SNP heritability in complex human traits (2017) PMID: 28530675
SumHer better estimates the SNP heritability of complex traits (2019) PMID: 30510236
Qualifications and specific competences:
The applicant must have an Bachelors degree and a MSC degree (or similar). As the project involves applied statistics, the applicant should have strong knowledge of statistics (e.g., a degree in mathematics, statistics, genetics or closely-related subjects). The applicant should ideally have some coding experience (e.g., in R, Stata, Matlab, C, etc). For applicants originally trained in mathematics or statistics, it will be desirable to have had previous experience in genetics, but not necessary. The applicant must be fluent in English, both oral and in writing.
Place of employment and place of work:
The PhD will be based in the QGG, which is a major center for research and education in quantitative genetics and quantitative genomics https://qgg.au.dk/en/. QGG is an international research center with about 70 employees and visiting researchers from over 15 nations. Its members perform research within human genetics, livestock and plant breeding. It has buildings in Foulum, Flakkebjerg and on the main campus in Aarhus center. The PhD candidate is expected to be at the Aarhus Campus.
Contacts:
Applicants seeking further information are invited to contact Este enderezo de correo está a ser protexido dos robots de correo lixo. Precisa activar o JavaScript para velo.
