PhD Student in Plant Science with Focus on Deep-Learning Conifer Genomics

3 weeks ago


Umeå, Västerbotten, Sweden Umeå University Full time

Project Overview

High-throughput DNA sequencing technologies have led to the production of large genome assemblies, including those of conifers, wheat, axolotl salamanders, and giant lungfish. Genome annotation, particularly the identification of transposable elements (TEs), presents a significant challenge in analyzing these large genomes. Deep-learning models, which can encode high-dimensional genome sequences into vectors and learn to resolve sequence complexity, offer promising solutions for efficient TE identification.

The PhD project focuses on plant genomics and the development of deep-learning driven computational tools. Key objectives include developing new deep-learning driven TEs identification tools to decrease computational demand for large genome analysis, constructing a comprehensive TE dataset to capture sequence diversity, and predicting TE insertion from genome sequences.

This PhD position is focused on developing and applying deep-learning driven methods to advance our understanding of TE complexities in large plant genomes. The successful candidate will work at the interface of machine-learning, computational biology, and molecular biology, collaborating with experimentalists and computational scientists across various disciplines.

Admission Requirements

To be eligible for this PhD position, applicants must have qualifications equivalent to a completed degree at the second-cycle level or completed course requirements of at least 240 ECTS credits, including at least 60 ECTS credits at the second-cycle level. Relevant experience in standard molecular biological techniques or machine-learning is also required.

Key Skills and Knowledge

The successful candidate should have an academic background in plant biology, machine-learning, bioinformatics, or a related field. Essential skills include experience in standard molecular biological techniques or machine-learning, very good written and oral English language skills, and proficiency in working with computers and programming languages such as R, Python, or Julia.



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