Found 4 relevant results in 2.62s where lecturer="Camille Pierre Albouy"
Environmental DNA (eDNA) allows the detection of organisms from traces of their DNA sampled from water, air or soil. Sampling eDNA instead of organisms makes monitoring fast, non-invasive, scalable and inexpensive. In this lecture, students will learn about eDNA and how it can be sampled, sequenced and analysed for biodiversity discovery and monitoring.
Students work in small groups to design a field based eDNA project along an environmental gradient, collect water samples using standardized methods, and submit filters for sequencing. A preparatory Zoom session introduces the sampling procedures. In January, students meet at ETH to process the sequencing data, explore biodiversity patterns, and develop management recommendations.
Students are introduced to a typical data science workflow using various examples from environmental systems. They learn common methods and key aspects for each step through practical application. The course enables students to plan their own data science project in their specialization and to acquire more domain-specific methods independently or in further courses.
Students are introduced to advanced data science where environmental data are analyzed using state of the art machine learning methods. Starting from known statistical approaches, they learn the principle of more advanced machine learning methods with practical application. The course enables students to plan their own data science project in their specialization and to apply machine learning mode