Deep Learning for Evolutionary Genomics (DLEG01)

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 evolutionary and population genomic data using modern neural network architectures and practical genomic workflows.

Deep learning is transforming evolutionary genomics by enabling researchers to detect complex patterns in genomic data that are often difficult to identify using conventional statistical or machine learning approaches. From identifying signatures of natural selection and predicting functional genomic elements to modelling population structure and evolutionary processes, deep learning is becoming an increasingly important tool in modern evolutionary research. This hands-on course provides practical training in applying neural networks to evolutionary genomic datasets using real-world examples.

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, and prediction, 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 population geneticists, Bioinformaticians and computational biologists, Researchers working with genomic and sequencing datasets, PhD students and quantitative life scientists, Anyone interested in applying artificial intelligence to evolutionary biology.

Why take this course?

As genomic datasets continue to grow in size and complexity, deep learning offers powerful new approaches for extracting biological insight from genome-scale data. Modern neural networks can model highly complex, non-linear relationships and are increasingly being used to investigate adaptation, population structure, variant classification, functional genomics, demographic history, and evolutionary processes.

This course equips you with the practical skills needed to design, train, evaluate, and interpret deep learning models for evolutionary genomics. Whether you're interested in population genomics, comparative genomics, conservation genetics, or evolutionary inference, 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