Education
Intensive bootcamps, workshops, and training in AI, machine learning, bioinformatics, and scientific computing.
Hands-on training in AI and scientific computing
Intensive programs for working researchers. No prior programming experience required for introductory offerings. In-person in San Diego.
Registration
Program registration is coming soon. Dates are scheduled based on demand; tell us what interests you and we will build the schedule around you.
Free Seminars and Resources
Explore topics before committing. Monthly talks and openly licensed materials available to everyone.
Public Seminar Series
Free, virtual, 1-hour evening sessions open to everyone. Topics align with paid programs and showcase real research applications of AI and computational methods. Hosted on Luma; recordings archived.
Schedule coming soon.
Open Educational Resources
Tutorials, code notebooks, datasets, and video lectures published under permissive licenses (CC-BY for content, MIT for code). Free to use, adapt, and redistribute.
Workshop Programs
Focused, single-day programs on specific skills. In person in San Diego; ESB AI Lab laptops, materials, and Certificate of Completion included.
Linux for Scientists
Learn the command line from scratch: navigation, text processing with grep/sed/awk, shell scripting, SSH, and Git basics. The recommended starting point before most bootcamp programs.
Introduction to Containers and Docker
Get started with Docker: pull images, run containers, write Dockerfiles, Docker Compose basics, and Singularity/Apptainer for HPC.
Docker for Reproducible Research
Design container strategies for making computational research reproducible: version-pinning, packaging pipelines, Zenodo archiving, and long-term reproducibility planning.
Introduction to Genetics and Genomics
A biology primer for computational people. Covers molecular biology fundamentals, genome organization, sequencing technologies, key genomics concepts, and current frontiers.
HPC and Cloud Computing for Research
Understand the research computing landscape. Covers Slurm, batch jobs, parallelization, and intro to AWS/cloud cost optimization.
Building AI Chatbots for Research
Build a domain-specific AI assistant using LLM APIs, prompt engineering, and retrieval-augmented generation (RAG) for your own documents.
3D Printing for Science and Prototyping
Design, slice, and print functional objects for your lab. Covers CAD (OpenSCAD, FreeCAD), FDM printing, material selection, and design for scientific applications.
Robotics for Scientific Data Collection
Build sensor systems and automated data collection platforms using Arduino, Raspberry Pi, and scientific sensors. Build a working prototype you take home.
Bootcamp Programs
Multi-day intensive programs with hands-on instruction. Flexible scheduling: weekday intensives, weekend intensives, half-day sessions, or evening series.
Applied Bioinformatics for Genomics Research
Go from raw sequencing data to biological insight. Covers Linux command line, sequence alignment, variant calling (GATK), RNA-seq analysis (DESeq2), and reproducible pipelines.
Python for Data Science and Machine Learning
Build a working foundation in Python, data analysis, and machine learning grounded in real scientific datasets. Python fundamentals through scikit-learn and intro deep learning with PyTorch.
AI/ML for Biological Applications
Apply modern machine learning to real biological problems: image classification, protein function prediction, computer vision for biology, transfer learning, model deployment, and AI ethics.
Data Science for Conservation and Agriculture
Work with environmental, agricultural, and ecological data using data science and AI tools. Covers geospatial data, remote sensing, time-series analysis, and edge AI for field sensors.
Deep Learning for Scientists
Build deep learning models from the ground up, starting with the math, moving through modern architectures, and ending with real applications. NumPy to PyTorch, CNNs, Transformers.
GPU Computing for Scientific Applications
Accelerate scientific computing with GPUs. Covers CUDA programming, Numba, CuPy, RAPIDS (cuDF, cuML), profiling, and multi-GPU setups. Hands-on with ESB AI Lab GPU servers.
Embedded AI with NVIDIA Jetson
Build and deploy AI applications on edge devices using NVIDIA Jetson and AGX Orin. Covers JetPack SDK, TensorRT, DeepStream, real-time CV pipelines, and power-constrained optimization.
Big Data with Apache Spark
Process and analyze datasets that do not fit on a single machine. PySpark, Spark SQL, MLlib, Spark Structured Streaming, performance tuning, and AWS EMR deployment.
Distributed Computing with Ray
Scale Python and ML workflows from a laptop to a cluster without rewriting code. Ray Core, Ray Data, Ray Train, Ray Tune, Ray Serve, and Kubernetes deployment.
Cloud Computing with AWS for Researchers
Amazon Web Services for research workloads. EC2, S3, Lambda, Batch, SageMaker, cost management, and reproducible research environments. Cloud credits provided.
Cloud Computing with GCP for Researchers
Google Cloud Platform for research. Compute Engine, BigQuery, Terra/Cromwell, Vertex AI, and cost optimization. Cloud credits provided.
Containers for Scientists
Master containers from first Dockerfile to production-grade deployment. Docker, Compose, Singularity/Apptainer for HPC, networking, storage, registries, CI/CD, and reproducible pipelines.
Networking for Scientists
How computer networks actually work. TCP/IP, DNS, SSH, firewalls, VPNs, network troubleshooting, lab server configuration, and cloud networking basics.
Advanced Containers: Building Research Infrastructure
Production-grade containerized services. Reverse proxies (Nginx/Traefik), authentication (OAuth2/SSO), API gateways, TLS/SSL, logging and monitoring, and security hardening.
Scholarships and Accessibility
ESB AI Lab is committed to broadening scientific participation.
Sliding-Scale Fees and Scholarships
All programs include a published sliding-scale fee structure and scholarship provisions for participants from underserved communities and minority-serving institutions.
Employer Reimbursement
Many employers cover professional development costs. We accept institutional purchase orders (net 30) and provide all documentation your HR or finance department needs for reimbursement.