Corporate Workshops

Master the foundations of robust numerical computing with focus on reproducibility, stability, and correctness.

• Floating-point arithmetic & numerical errors
• Algorithm stability & conditioning
• Linear algebra best practices
• Reproducibility frameworks

Duration: 2 days

Format: Hands-on workshop

Prerequisites: Basic programming knowledge

Unlock Julia's performance capabilities through parallel computing, multi-threading, and optimization techniques.

• Performance profiling & benchmarking
• Multi-threading & parallelism
• GPU computing with CUDA.jl
• Type stability & compiler optimization

Duration: 3 days

Format: Intensive hands-on lab

Prerequisites: Julia basics, linear algebra

Learn to effectively leverage large language models and AI assistants to accelerate scientific software development while maintaining code quality and correctness.

• Prompt engineering for code generation & debugging
• AI-assisted refactoring & documentation
• Testing AI-generated scientific code
• Best practices for LLM-human collaboration
• Limitations & validation strategies

Duration: 2 days

Format: Interactive workshop with hands-on exercises

Prerequisites: Basic programming knowledge, familiarity with Python or Julia

Integrate physics-informed ML and differential equation solvers into research workflows.

• Physics-informed neural networks (PINNs)
• Neural ODEs & differential equation solvers
• Uncertainty quantification
• Model validation & benchmarking

Duration: 4 days

Format: Intensive hands-on training

Prerequisites: ML basics, calculus, linear algebra

Level up your research code collaboration with industry-standard version control practices.

• Advanced Git workflows (branching, merging, rebasing)
• Code review best practices & pull request etiquette
• Collaborative development standards
• Basic CI/CD concepts for scientific software

Duration: 1 day

Format: Workshop + Hands-on Practice

Prerequisites: Basic familiarity with version control

Move from Jupyter/MATLAB prototypes to enterprise-grade production systems.

• Modular architecture design
• API development & deployment
• Data pipeline engineering
• Monitoring & observability

Duration: 3 days

Format: Workshop with live refactoring

Prerequisites: Research code in need of productionization

Design scalable, maintainable systems using domain-specific languages (DSLs), monorepo strategies, and decoupling patterns.

• Domain-specific language (DSL) design
• Monorepo architecture & tooling
• Dependency inversion & decoupling
• System integration patterns

Duration: 4 days

Format: Architecture workshop + case studies

Prerequisites: Software engineering experience

Build robust, reliable scientific software through systematic testing strategies and test-driven development.

• Test-driven development (TDD) methodology
• Testing strategies (unit, integration, end-to-end)
• Test coverage metrics and quality standards
• Testing scientific computations and numerical accuracy
• Mocking, fixtures, and test data management
• Continuous testing in CI/CD pipelines

Duration: 2 days

Format: Workshop + Hands-on TDD Sessions

Prerequisites: Working knowledge of at least one programming language

Master the fundamentals of treating images as numerical arrays and building reproducible analysis pipelines. Eliminate "point-and-click" workflows in favor of batch-processable, version-controlled code.

• NumPy array operations & image representation
• Scikit-image for filtering and segmentation
• Batch processing strategies & file I/O
• Pandas integration for quantitative results
• Quality control & validation metrics

Duration: 3 days

Format: Hands-on workshop

Prerequisites: Basic Python knowledge

Learn to build and deploy custom analysis tools within Napari, the standard multi-dimensional image viewer for Python. Create production-grade plugins that bridge the gap between computational methods and end-user accessibility.

• Napari architecture & plugin ecosystem
• MagicGUI for rapid interface development
• Widget design patterns & user workflows
• Testing & documentation standards
• PyPI deployment & version management

Duration: 2 days

Format: Hands-on workshop

Prerequisites: Python proficiency, basic image analysis experience

A comprehensive deep dive into Deep Learning for microscopy imaging. Learn to train, validate, and deploy custom segmentation models using modern architectures. Emphasis on ground truth quality and avoiding overconfident predictions.

• Ground truth annotation strategies & quality control
• Model architecture selection (StarDist, Cellpose, U-Net)
• Cloud-based training pipelines (GPU optimization)
• Validation metrics & error analysis
• Production inference deployment & monitoring

Duration: 4 days

Format: Hands-on workshop

Prerequisites: Python, NumPy, basic machine learning concepts

Go beyond standard architectures and delve into the cutting edge of deep learning for scientific imaging. This course focuses on designing and implementing novel solutions for challenging imaging problems.

• Custom CNN and transformer architectures
• Self-supervised and unsupervised learning for imaging data
• Multimodal data fusion (e.g., imaging + sequencing)
• Advanced image segmentation techniques (e.g., panoptic segmentation)
• Generative models for image restoration and augmentation

Duration: 5 days

Format: Hands-on workshop

Prerequisites: Python, PyTorch, experience with deep learning models

Learn how to translate biological questions into mathematical models. This course covers the fundamentals of building, simulating, and analyzing models of biological systems, from single cells to populations.

• Ordinary differential equation (ODE) based modeling
• Parameter estimation and sensitivity analysis
• Bifurcation analysis and multistability
• Stochastic modeling of biological processes
• Network analysis of biological pathways

Duration: 4 days

Format: Hands-on workshop

Prerequisites: Python, calculus, linear algebra, basic biology concepts