Deep Learning for Evolutionary Genomics (DLEG01) – Applications for Evolutionary Biology
 https://prstats.org/course/deep-learning-for-evolutionary-genomics-dleg01/
Delivered by experienced computational biologists, evolutionary geneticists, and deep learning researchers.
Learn how to apply deep learning to genomic data, with methods that are transforming evolutionary biology, population genetics, and evolutionary genomics.
Advances in sequencing technologies have produced genomic datasets of unprecedented size and complexity. Deep learning is rapidly emerging as a powerful approach for identifying complex patterns that are difficult to detect using traditional statistical methods, enabling researchers to investigate adaptation, natural selection, demographic history, population structure, speciation, and evolutionary processes with greater accuracy and flexibility. While this course focuses on deep learning for evolutionary genomics, the methods are applicable across a broad range of evolutionary research.
What you'll gain
- A strong understanding of deep learning concepts and neural network architectures
- Practical experience applying deep learning to genomic datasets
- Skills in supervised learning, classification, prediction, and feature extraction
- Understanding of model training, optimisation, validation, and performance assessment
- Confidence in interpreting deep learning models and applying them to evolutionary research
Course format
- Live, instructor-led online training
- Hands-on coding with real-world genomic datasets
- Interactive practical exercises throughout
- Strong focus on applied, research-ready workflows
Who is this course for?
- Evolutionary biologists and evolutionary ecologists
- Population geneticists and evolutionary genomic researchers
- Bioinformaticians and computational biologists
- Researchers working with genomic and sequencing datasets
- PhD students and quantitative life scientists
Why take this course?
Deep learning is opening new opportunities for evolutionary biologists to analyse genome-scale datasets and address questions that were previously difficult to investigate using conventional analytical approaches. These methods are increasingly used to identify signatures of natural selection, infer demographic history, classify genomic variation, investigate adaptation, predict functional genomic elements, and uncover the evolutionary processes shaping genetic diversity.
This course equips you with the practical skills needed to design, train, evaluate, and interpret deep learning models for evolutionary genomics. Whether you're studying adaptation, speciation, phylogeography, population divergence, or evolutionary history, you'll gain the computational toolkit needed to apply modern AI methods confidently to your own research.
Learn more & enrol
PR Stats course page for Deep Learning for Evolutionary Genomics (DLEG01)
https://prstats.org/course/deep-learning-for-evolutionary-genomics-dleg01/
Questions?
Email: oliver@prstats.org

Oliver Hooker
Managing Partner
Advanced Training for Researchers in the Life Sciences
coursesinfo@prstats.org | www.prstats.org

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