Third-cycle subject: Computer Science
The project focuses on advancing species delimitation in biodiversity conservation using modern artificial intelligence (AI) methodologies. Based on morphological traits, traditional species delimitation methods struggle with groups like bacteria and moss, where morphology is either highly plastic or non-variable. To document and conserve the numerous undocumented species, many of which are nearing extinction, the project proposes leveraging AI advancements for improved species delimitation.
Large-scale initiatives like the International Barcode of Life and the Earth Biogenome Project are already working on characterizing and sequencing the DNA of millions of species. However, computational challenges exist in using these massive genomics datasets for species delimitation. The multispecies coalescent (MSC) model offers a framework for understanding genealogical relationships and genetic divergence across species, but its complexity requires new methodologies for efficient analysis.
This project aims to use Variational Inference (VI) methods, enhanced by AI technologies like normalizing flows, graph neural networks, and transformers to represent distributions over trees, to improve MSC estimation. These technologies have shown significant improvements in phylogenetic analysis over traditional Bayesian methods, and this capacity for improvements will have substantially more impact on the more complex MSC model.
The project will develop an efficient framework for constructing species and gene trees under the MSC model. It aims to achieve a genome-wide analysis capability, including all gene families of considered species and many species. Finally, a framework that exploits the coalescent model for species delimitation will be developed and applied.
Supervision: Jens Lagergren is proposed to supervise the doctoral student. Decisions are made on admission
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Type of employment | Temporary position |
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Contract type | Full time |
First day of employment | At soon as possible or by agreement |
Salary | Monthly salary according to KTH's doctoral student salary agreement |
Number of positions | 1 |
Full-time equivalent | 100% |
City | Stockholm |
County | Stockholms län |
Country | Sweden |
Reference number | PA-2025-0811 |
Contact |
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Published | 13.Mar.2025 |
Last application date | 15.May.2025 11:59 PM CEST |