PhD position in Computational Phylogenetics 100 %

About the job

PhD position in Computational Phylogenetics 100 %

In this exciting PhD project, you pioneer neuro-symbolic methods that retain the mechanistic grounding of classical phylogenetics, and that integrate the representational richness of genomic LLMs.

School: Life Sciences und Facility Management

Starting date: 01.09.2026 or upon mutual agreement

Genomic sequences are modeled as evolving along binary phylogenetic trees through stochastic string-valued substitution and insertion-deletion (indel) processes. Given a set of present-day sequences, classical inference problems in phylogenetics are: (i) homology inference (ii) tree inference, and (iii) ancestral sequence reconstruction. A central focus of our recent work has been to develop fast frequentist indel-aware approaches to these problems.

For tractability, the models in most cases must assume that residues evolve independently across sites. In reality, mutation probabilities are influenced by sequence context, including position-specific structural and functional constraints. In recent years, the convergence of computational biology and data-driven methods has led to genomic large language models (gLLMs). These can model sequence context dependences.

Building on our previous work, our aim is to develop neuro-symbolic methods that retain mechanistic grounding of classical phylogenetics, and that integrate the representational richness of gLLMs. As a PhD student you will devise mutation models, develop inference algorithms, implement them in our Rust code-base, and evaluate the methods by simulation and on real data.

Selection of relevant articles:

1. Maiolo M, Zhang X, Gil M, Anisimova M. "Progressive multiple sequence alignment with indel evolution" BMC Bioinformatics. 2018. 19(1):331. doi: 10.1186/s12859-018-2357-1.

2. Pečerska, J., Gil, M. and Anisimova, M. “Joint alignment and tree inference" bioRxiv, 2021. pp.2021-09. doi: 10.1101/2021.09.28.462230.

3. Jowkar, G., Pečerska, J., Maiolo, M., Gil, M., & Anisimova, M. “ARPIP: Ancestral sequence Reconstruction with insertions and deletions under the Poisson Indel Process" Systematic biology. 2022. syac050-syac050. doi: 10.1093/sysbio/syac050

4. Iglhaut C, Pečerska J, Gil M, Anisimova M. "Please Mind the Gap: Indel-Aware Parsimony for Fast and Accurate Ancestral Sequence Reconstruction and Multiple Sequence Alignment Including Long Indels" Molecular Biology and Evolution. 2024. 41(7):msae109. doi: 10.1093/molbev/msae109.

You should have a MSc in Computer Science, Computational Science, Computational Biology, Statistics / Applied Mathematics, or a related quantitative field, with a strong background in:

• Algorithms, particularly combinatorial optimization
• Stochastic modelling
• Computational inferential statistics
• Programming, ideally in Rust and/or C++

Knowledge of phylogenetics, and/or an understanding of neural networks is an advantage.

Zurich University of Applied Sciences ZHAW is one of Switzerland's largest multidisciplinary universities of applied sciences, with over 14'000 students and 3'400 faculty and staff.

Life Sciences and Facility Management

Study and research in Wädenswil: practical, creative, passionate and reflective. Our expertise in life sciences and facility management in the areas of the environment, food and health enables us to make a vital contribution to solving social challenges and improving quality of life.

The PhD position is based at the Research Centre for Bioinformatics co-headed by Dr. Manuel Gil and Prof. Maria Anisimova. It is funded by a DIZH Senior Fellowship awarded to Dr. Manuel Gil. The PhD student will be formally enrolled at the University of Zurich. Our centre is part of the Swiss Institute of Bioinformatics, which provides additional training and networking opportunities

ZHAW is committed to gender-mixed and diverse teams in order to promote equality, diversity and innovation.

Wir bieten hochschulgerechte Arbeits- und Anstellungsbedingungen an und fördern aktiv die Personalentwicklung unserer Mitarbeitenden und Führungspersonen. Eine detaillierte Beschreibung der Vorteile ist auf der Webseite «Arbeiten an der ZHAW» zu finden. Hier die wichtigsten Eckpunkte:

Dr. Manuel Gil

Co-Leiter FS Bioinformatics

Jocelyn Schaad

Recruiting Manager