Deep Learning for Evolutionary Genomics (DLEG01) – Applications for Evolutionary Biology https://prstats.org/course/deep-learning-for-evolutionary-genomics-dleg01/ Only 4 places left! Delivered by Dr. Nikolay Oskolkov, a bioinformatician, computational biologist, and data scientist working at the intersection of biology, statistics, and artificial intelligence. His expertise includes machine learning, deep learning, population genomics, ancient DNA, metagenomics, dimensionality reduction, and computational biology. Learn how to apply deep learning and neural networks to evolutionary and genomic datasets, with methods highly applicable to evolutionary genomics, population genetics, molecular evolution, adaptation, introgression, functional genomics, and ancient DNA research. Modern evolutionary research increasingly involves large, complex, and high-dimensional genomic datasets. Deep learning provides powerful approaches for identifying patterns in genomic sequences, extracting structure from population genomic data, detecting introgressed regions, analysing ancient DNA, identifying functional genomic elements, and modelling biological sequences. This course provides practical training in these methods using examples drawn directly from evolutionary biology and genomics. What you'll gain An understanding of the theoretical foundations of artificial neural networks and deep learning Practical experience implementing deep learning approaches in R and Python Understanding of feed-forward neural networks, CNNs, RNNs, and LSTM networks Applications of neural networks to population and evolutionary genomics Use of feed-forward neural networks to identify introgressed genomic regions Experience using autoencoders for dimensionality reduction and representation learning Comparison of autoencoders with PCA, t-SNE, and UMAP Applications of CNNs to gene annotation, promoter prediction, enhancer detection, and functional genomic analysis Approaches for analysing DNA using natural-language-processing concepts Deep-learning applications to ancient DNA and ancient-status inference Understanding of transformer architectures and their applications to biological sequence modelling Experience extracting biological patterns from complex, high-dimensional genomic datasets Course format 5-day live, instructor-led online course 25 hours of training Combination of lectures and hands-on practical exercises Practical implementation in R and Python Examples from evolutionary biology, population genomics, functional genomics, metagenomics, and ancient DNA Opportunity to discuss participants' own data All code, datasets, and presentation materials provided All live sessions recorded 30 days of recording access and post-course email support Who is this course for? Evolutionary biologists Evolutionary and population geneticists Evolutionary genomic researchers Molecular evolution researchers Researchers studying adaptation, introgression, and population history Ancient DNA and palaeogenomic researchers Functional genomic researchers Bioinformaticians and computational biologists Postgraduate students and early-career researchers Participants should have a basic background in R or Python and some familiarity with biological datasets. A foundation in statistics, probability, and machine learning is helpful but not required, and prior experience with neural networks or deep learning is not necessary. Why take this course? Evolutionary genomics increasingly relies on datasets containing millions of genetic variants, complex sequence information, and high-dimensional representations of individuals and populations. Extracting evolutionary information from these datasets presents computational and statistical challenges that are particularly well suited to modern machine-learning approaches. Deep learning can identify nonlinear patterns and complex relationships that may be difficult to capture using conventional methods. In population genomics, autoencoders can be used for representation learning and dimensionality reduction, while neural networks can be applied to problems such as identifying introgressed genomic regions and reconstructing aspects of population history. The course also explores applications to functional genomics and ancient DNA, including CNN-based identification of functional genomic elements and deep-learning approaches for ancient-status inference. Transformer architectures extend these ideas to biological sequence modelling, providing an introduction to some of the newest AI approaches being applied to genomic research. Whether you're investigating population history, adaptation, introgression, genomic variation, ancient DNA, functional evolution, or biological sequence evolution, this course provides a practical foundation for incorporating modern deep-learning methods into evolutionary research. Course dates 5–9 October 2026 25 hours | Live online | £400 Learn more & enrol https://prstats.org/course/deep-learning-for-evolutionary-genomics-dleg01/ Questions? Email: oliver@prstats.org (to subscribe/unsubscribe the EvolDir send mail to evoldir@evoldir.net)