Generative AI for Life Science
Learn about generative AI applications in life sciences, including cell fate prediction with autoencoders, protein language models, and vision language models for medical imaging.
Browse our collection of AI learning materials
Learn about generative AI applications in life sciences, including cell fate prediction with autoencoders, protein language models, and vision language models for medical imaging.
Participate in Mimer AI hackathons and collaborative coding events
Materials for the Kubernetes workshop @ MIMER / ENCCS. Learn to deploy and manage containerized applications on Kubernetes.
Introduction into agentic coding, agent harnesses, context management, tool-calling, MCP, and ACP.
Deep learning is a powerful subset of machine learning where computers learn patterns from data, similar to how our brains learn. It uses artificial neural networks - systems inspired by biological neurons that process information through many layers. This beginner-friendly workshop, organized by Mimer in partnership with LUMI AI Factory, provides an introduction to deep learning concepts, workflows, architectures, and practical applications.
MLOps (Machine Learning Operations) combines machine learning, software engineering, and DevOps to reliably build, deploy, monitor, and maintain ML models in production. This 3-half-day event covers the entire MLOps pipeline with interactive lessons and hands-on labs.
Learn parallel computing techniques with PyTorch for distributed training across multiple GPUs.
Julia is a modern programming language offering high performance comparable to C and Fortran without sacrificing simplicity. This lesson covers data formats, DataFrames, linear algebra, data science, machine learning, regression, and time-series prediction.
5-day workshop covering multi-GPU AI training, including PyTorch Distributed Data Parallel, model parallelism, PyTorch Lightning, fine-tuning neural networks, computer vision, MLOps on HPC, Ray, RAG, and hyperparameter tuning.
A practical framework for researchers who want to use AI coding assistants responsibly. Covers three scenarios of increasing automation: chat-based coding, IDE integration, and full agentic code development.