Re-advertisement: Decipher Complex Phenotypes via Cross-species Analysis of the Regulatory Genome

Technical Sciences - Center for Kvantitativ Geneti; Aarhus; On-site; DKK 357,000 - 402,000; Full time

Job summary

Aarhus University invites applications for a PhD fellowship in the Quantitative Genetics and Genomics programme. The project aims to decipher regulatory logic through cross-species analysis of the regulatory genome, leveraging AI frameworks to identify functional variants across species.

The successful candidate will join a high-impact, international research group, work with large multi-omics datasets, and contribute to manuscripts published in premier journals.

Qualifications

• Master’s degree in Statistical Genetics, Bioinformatics, Computational Biology, Computer Science, Mathematics, or closely related quantitative field.
• Proficiency in Python or R for genomic algorithms.
• Experience with machine learning or deep learning frameworks (e.g., PyTorch, TensorFlow) is desirable.
• Strong interest in human/animal genetics or comparative genomics.

Responsibilities

• Develop novel AI frameworks to identify functional genetic variants influencing gene regulation across species.
• Perform cross-species comparative analyses of regulatory patterns in farmed animals and humans.
• Map regulatory mechanisms to complex traits to improve predictive accuracy in clinical and agricultural settings.
• Contribute to drafting high-impact manuscripts and collaborate in international consortia.

Skills

Analytical thinking

Team collaboration

Scientific writing

Education

Master's degree in Statistical Genetics or closely related field

Tools

Python

R

PyTorch

TensorFlow

Job description

Decipher Complex Phenotypes via Cross-species Analysis of the Regulatory Genome

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 01 January 2027 or later.

Research area and project description

A fundamental challenge in modern genetics is decoding how non-coding genetic variation regulates gene expression to shape complex phenotypes. Despite the wealth of GWAS data, the lack of functional insights remains a significant barrier to the advancement of genomic selection in precision agriculture and the implementation of personalized medicine in humans.

Objectives

This PhD project seeks to revolutionize our understanding of regulatory logic by:

• 1. Developing Novel AI Frameworks: Building state-of-the-art computational models to identify functional genetic variants that govern gene regulation across diverse species, including farmed animals and humans.
• 2. Cross-Species Comparative Analysis: Investigating the evolutionary conservation and divergence of gene regulatory patterns between farmed animals and humans.
• 3. Mapping the Genetic Landscape: Elucidating how these regulatory mechanisms orchestrate the architecture of complex traits to improve predictive accuracy in both clinical and agricultural settings.

Research Environment

The successful candidate will join a high-impact international research group with a track record of publishing in premier journals such as Nature Genetics and Cell Genomics. You will leverage large-scale multi-omics datasets to bridge the gap between DNA sequence and function.

Relevant publications

https://www.nature.com/articles/s41588-025-02121-5https://www.nature.com/articles/s41588-023-01585-7https://www.nature.com/articles/s41588-025-02155-9;https://www.nature.com/articles/s41588-025-02329-5https://www.nature.com/articles/s41588-025-02329-5https://www.cell.com/cell-genomics/fulltext/S2666-979X(23)00187-8

Qualifications and specific competences

The successful candidate must hold a master’s degree in Statistical Genetics, Bioinformatics, Computational Biology, Computer Science, Mathematics, or a closely related quantitative field.

Technical & Professional Competencies

• Computational Proficiency: Advanced programming skills in Python or R are essential for developing and implementing genomic algorithms.
• Analytical Expertise: Prior experience with machine learning or deep learning frameworks (e.g., PyTorch, TensorFlow) is highly desirable, particularly in the context of sequence-to-function modeling.
• Scientific Drive: A strong intellectual interest in human/animal genetics or comparative genomics, with a desire to decode the regulatory logic of complex traits.
• Communication Excellence: Proficiency in written and spoken English is required, as the role involves drafting high-impact manuscripts and collaborating across international research consortia.

Place of employment and place of work

The place of employment is Aarhus University, and the place of work is C. F. Møllers Allé 3, bld. 1130, 8000, Aarhus, Denmark.

Contacts

• Lingzhao Fang, (main supervisor)
• Goutam Sahana, (co-supervisor)
• Peter Sørensen, (co-supervisor)

Application deadline is 25 October 2026 at 23:59 CEST.

Preferred starting date is 01 January 2027.

Please note

• Only documents received prior to the application deadline will be evaluated. Thus, documents sent after deadline will not be considered.
• 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.

Aarhus University’s ambition is to be an attractive and inspiring workplace for all and to foster a culture in which everyone 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.

Salary and terms of employment are in accordance with applicable collective agreement.