1. PhD thesis in microbial community assembly (computational ecology and evolution) A Ph.D.studentship in computational biology is available in the laboratory of Andreas Wagner at the University of Zurich. We are looking for a researcher to study the assembly of microbial communities using computational modeling. Traditional mathematical and computational models do not take into account that organisms have a rich internal structure. For example, even the simplest bacterium has a complex metabolism in which hundreds of enzymes determine which foods the bacterium can consume, which defense molecules it produces, and which waste products it excretes. Traits like these determine in which environments an organism survives and which kinds of communities it can form. They can be predicted for many species with the aid of genome-scale metabolic models. The project will use genome-scale metabolic models to predict the assembly, stability, diversity, and composition of microbial communities from first principles of metabolic biology. It may also study how ecological and evolutionary processes interact during community assembly. The kinds of questions it will ask include how the structure of an ecological community depend on its history. Do communities become more resilient to perturbations over time? Why do simple rules fail to predict the stability of complex communities? How does ongoing evolution of a community's organisms influence the structure and stability of the community? The successful candidate will be expected to shape their own project within this research area. They will have strong mathematical or computational skills, and a background in biology, bioinformatics, computational biology, biochemistry, biophysics, or related subjects. Fluency in a major programming language, such as python is essential. Familiarity with computational models to analyze complex metabolic systems, such as Flux Balance Analysis is a plus. Applications without a demonstrated interest or research history in fundamental ecological or evolutionary questions will not be considered further. We are looking for an individual with a Masters Degree or equivalent, who is highly self-motivated and can work independently. Lab members have diverse backgrounds and research projects but are unified by their interests in life's fundamental organizational principles. Ongoing projects cover a broad range of topics in evolution and at the interface of ecology and evolution, such as the dynamics of microbial community assembly, the evolution of multicellularity, and the structure of adaptive landscapes (e.g., Wagner, Molecular Ecology2022; Papkou et al. Science 2023). The working language in the laboratory is English. German skills, although helpful, are not essential. Zurich is a highly attractive city in beautiful surroundings, with a multinational population, and many educational and recreational opportunities. To be considered, please send a single (!) PDF file merged from the following parts to jobs.wagner@ieu.uzh.ch. CV including publication list, academic transcripts, a statement of research interests not exceeding three pages, and contact information for three academic references. Please include the word "PHD24COMM" in the subject line. Applications will be considered until May 10, 2024. The position is available from Summer 2024. 2. PhD thesis in machine learning and evolutionary biology A Ph.D.studentship in computational biology is available in the laboratory of Andreas Wagner at the University of Zurich. We are looking for a researcher to develop and use machine learning methods to understand the topography of experimentally mapped fitness landscapes. Fitness landscapes are analogues of physical landscapes in which a location corresponds to a genotype and the elevation of that location corresponds to fitness. The topography of such landscapes is crucial to determine how and whether Darwinian evolution enables populations to reach high fitness. For many years, such landscapes have been studied only theoretically, but experimental techniques such as CRISPR-Cas genome editing have permitted measuring the fitness of thousands of microbial genotypes. Experimentalists in our lab have used this and other techniques to map experimental fitness landscapes, but our ability to predict the structure of such landscapes is rudimentary. For example, we do not know how sparse sampling of fitness data may affect our ability to predict landscape topography. We also cannot predict how landscape topography depends on the environment. The subject of this project is to develop appropriate machine learning methods to answer this and related questions for experimentally mapped fitness landscapes. The successful candidate will be expected to shape their own project within this research area. They will have strong mathematical or computational skills, and a background in computer science, biology, bioinformatics, biochemistry, biophysics, or related subjects. Fluency in a major programming language, such as python is essential. Familiarity with machine learning methods is a plus. We are looking for an individual with a Masters Degree or equivalent, who is highly self-motivated and can work independently. Lab members have diverse backgrounds and research projects but are unified by their interests in life's fundamental organizational principles. Ongoing projects cover a broad range of topics in evolution and at the interface of ecology and evolution, including the structure of adaptive landscapes (e.g., Papkou et al. Science 2023; Wagner, bioRxiv 2024.01.18.576262). The working language in the laboratory is English. German skills, although helpful, are not essential. Zurich is a highly attractive city in beautiful surroundings, with a multinational population, and many educational and recreational opportunities. To be considered, please send a single (!) PDF file merged from the following parts to jobs.wagner@ieu.uzh.ch. CV including publication list, academic transcripts, a statement of research interests not exceeding three pages, and contact information for three academic references. Please include the word "PHD24ML"the subject line. Applications will be considered until May 10, 2024. The position is available from Summer 2024. 3. PhD thesis on fitness landscapes (computational evolutionary biology) A Ph.D.studentship in computational biology is available in the laboratory of Andreas Wagner at the University of Zurich. We are looking for a researcher to study how populations evolve on experimentally mapped fitness landscapes. Fitness landscapes are analogues of physical landscapes in which a location corresponds to a genotype and the elevation of that location corresponds to fitness. The topography of such landscapes is crucial to determine how and whether Darwinian evolution enables populations to reach high fitness. For many years, such landscapes have been studied only theoretically, but experimental techniques such as CRISPR-Cas genome editing have permitted measuring the fitness of thousands of microbial genotypes. Experimentalists in our lab have used this and other techniques to map experimental fitness landscapes, but our understanding of how populations would evolve on the landscapes we mapped is still very incomplete. In particular we do not know how environmental change affects adaptive evolution on a changing fitness landscape.The subject of this project is to study this and related questions for experimentally mapped fitness landscapes. How does the success of Darwinian evolution depend on landscape topography? How strongly does landscape structure change when an environment changes?How does the frequency of environmental change affect Darwinian evolution on a landscape? The successful candidate will be expected to shape their own project within this research area. They will have strong mathematical or computational skills, and a background in biology, bioinformatics, computational biology, biochemistry, biophysics, or related subjects. Fluency in a major programming language, such as python is essential. Familiarity with population genetic theory and evolutionary dynamic simulations is a plus. Applications without a demonstrated interest or research history in fundamental ecological or evolutionary questions will not be considered further. We are looking for an individual with a Masters Degree or equivalent, who is highly self-motivated and can work independently. Lab members have diverse backgrounds and research projects, but are unified by their interests in life's fundamental organizational principles. Ongoing projects cover a broad range of topics in evolution and at the interface of ecology and evolution, such as the structure of adaptive landscapes, the evolution of multicellularity, and the dynamics of microbial community assembly (Papkou et al. Science 2023; e.g., Wagner, Molecular Ecology.2022). The working language in the laboratory is English. German skills, although helpful, are not essential. Zurich is a highly attractive city in beautiful surroundings, with a multinational population, and many educational and recreational opportunities. To be considered, please send a single(!) PDF file merged from the following parts to jobs.wagner@ieu.uzh.ch. CV including publication list, academic transcripts, a statement of research interests not exceeding three pages, and contact information for three academic references. Please include the word "PHD24FITNESS" in the subject line. Applications will be considered until May 10, 2024. The position is available from Summer 2024. IEU wagnerjobs (to subscribe/unsubscribe the EvolDir send mail to golding@mcmaster.ca)