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PhD Position in Computational Genetics and Machine Learning: analysis of multi-omics biological data in novel populations of Brachypodium

University of Aarhus - Graduate School of Technical Sciences

Qualification Type: PhD
Location: Aarhus - Denmark
Funding for: UK Students, International Students
Funding amount: Not Specified
Hours: Full Time
Placed On: 10th August 2022
Closes: 30th September 2022

Applicants are invited for a PhD fellowship/scholarship at Graduate School of Technical Sciences, Aarhus University, Denmark, within the Quantitative Genetics and Genomics programme. The position is available from 2 January 2023 or later. 

Title:
Re-advertisement: PhD position in computational genetics and machine learning: analysis of multi-omics biological data in novel populations of Brachypodium

Research area and project description:
We are looking for a talented and creative PhD student to contribute to the SIEVE project (“Selection of mutations by in silico and experimental variant effects: a new strategy to improve fitness in cool-season grasses”). The SIEVE project is supported by an Emerging Investigator grant by the Novo Nordisk Foundation, which funds groundbreaking research with promising applications in data science and the green transition in Europe.

The PhD student will be involved in an innovative research project in computational genetics and machine learning applied to a model species (Brachypodium). She/he will evaluate the impact of mutations by analyzing multi-omics data in mutant populations: (i) whole-genome sequencing; (ii) RNA expression; (iii) metabolomics; and (iv) physiological measurements. These analyses will benefit from expertise in machine learning (applied statistics, deep learning, network analysis) and computational genetics (quantitative genetics, evolutionary genomics, bioinformatics).

Under the SIEVE project, we will detect impactful mutations by machine learning and evolutionary genomics across species. We will focus on cool-season grass species, which include the model species Brachypodium as well as important crop species like wheat and barley. A key contribution of the project will be the validation of detected mutations at single-site resolution, in novel populations of Brachypodium. The selected PhD candidate will lead this effort of validation, by analyzing multi-omics data in mutant populations, and additional datasets in natural populations.

We encourage candidates to submit a description of proposed research for the validation of predicted genetic effects in mutant or natural populations. Candidates are welcome to contact Guillaume Ramstein (ramstein@qgg.au.dk) for information about the SIEVE project or this PhD position. 

Qualifications and specific competences:

Candidates must hold (or be expected to hold) a Master’s degree in a field related to computational genetics (e.g., quantitative genetics, evolutionary genomics, bioinformatics) or machine learning (e.g., applied statistics, deep learning, network analysis).

The ideal candidate will also demonstrate some knowledge of scripting/programming languages (e.g., in R, Python, Java or Shell), previous research experience in relevant fields, and good communication skills in English. 

Place of employment and place of work:
Center for Quantitative Genetics and Genomics, Aarhus University, Ole Worms Allé 3, Building 1171, 8000 Aarhus, Denmark 

Contacts:
Applicants seeking further information are invited to contact:
Guillaume Ramstein (ramstein@qgg.au.dk) for information about the SIEVE project or this PhD position. 

How to apply:
Please follow this link to submit your application. Application deadline is 30 September 23:59 CEST. Preferred starting date is 2 January 2023.

For information about application requirements and mandatory attachments, please see our application guide.

Please note:

  • The programme committee may request further information or invite the applicant to attend an interview.
  • Shortlisting will be used, which means that the evaluation committee only will evaluate the most relevant applications.
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