Applications are invited for a PhD fellowship/scholarship at Graduate School of Natural Sciences, Aarhus University, Denmark, within the Mathematics programme. The position is available from August 2023 or later.
Developing statistical learning methods for decoding mutational processes in large-scale genomic data sets
Research area and project description:
The purpose of this project is to develop an inference framework to model and analyse the mutational processes occurring in sperm cells.
The candidate will work on developing and applying machine learning methods to analyse data from testes and sperm samples. The relative weight of statistical modeling, methods development and data analysis will depend on the interests of the candidate
Most de novo mutations (>70%) originate from the father. We do not know why, when and how novel mutations arise during spermatogenesis, why older fathers pass on different types of mutations, and what influence the environment has. A significant proportion (>50%) of severe psychiatric diseases and male infertility cases are due to de novo mutations.
The analysis has been intractable due to the prohibitive costs of very deep sequencing data on different testis compartments. This has now changed, and in this project we develop new approaches to infer the mutational processes, and use this insight to make functional inference and prediction.
We are currently creating mutational sperm data from unique samples. We now wish to develop new mathematical and computational approaches inspired by cancer mutation modelling (non-negative matrix factorization) to understand the mutational processes occurring in sperm cells.
The final prediction model can directly aid clinical choices based on sequencing sperm alone. Non-invasive intervention to alleviate severe de novo disease can then become a reality.
The project is funded by the Novo Nordisk foundation as an interdisciplinary Data Science collaborative project between the Department of Growth and Reproduction at Copenhagen University Hospital (Kristian Almstrup), and Aarhus University with the Department of Mathematics (Asger Hobolth), the Department of Molecular Medicine (Søren Besenbacher) and the Bioinformatics Research Centre (Mikkel Heide Schierup & Thomas Bataillon).
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Qualifications and specific competences:
Applicants must have a relevant Master’s degree or at least one year of a Master’s degree in data science, statistics, mathematics, computer science or bioinformatics. The PhD study can be three years (with an MSc) or four years (with one year of an MSc completed at the time of enrollment).
Place of employment and place of work:
The place of employment is Aarhus University, and the place of work is Department of Mathematics, Aarhus University, Ny Munkegade 118, DK-8000 Aarhus C, Denmark.
Applicants seeking further information for this project are invited to contact: Professor Asger Hobolth, email@example.com
How to apply:
For information about application requirements and mandatory attachments, please see the Application guide. Please read the Application guide thoroughly before applying.
When ready to apply, go to https://phd.nat.au.dk/for-applicants/apply-here/ (Note, the online application system opens 1 March 2023)
- Choose May 2023 Call with deadline 1 May 2023 at 23:59 CET.
- You will be directed to the call and must choose the programme “Mathematics”.
- In the boxed named “Study”: In the dropdown menu, please choose: “Developing statistical learning methods for decoding mutational processes in large-scale genomic data sets (Dslmdm)”
- The programme committee may request further information or invite the applicant to attend an interview.
Aarhus University’s ambition is to be an attractive and inspiring workplace for all and to foster a culture in which each individual has opportunities to thrive, achieve and develop. We view equality and diversity as assets, and we welcome all applicants. All interested candidates are encouraged to apply, regardless of their personal background.
Read the job description at the university homepage
Post expires on Monday May 1st, 2023