Research & Scientific Computing
Research-driven development is part of who we are. Our founder’s doctoral work in graph neural networks and brain connectome analysis informs how we help researchers turn methods into reproducible, well-engineered software.
Problems we address
Challenges we help solve.
- Analysis pipelines that only one person can run
- Experiments that are hard to reproduce months later
- Imaging and signal data too large for ad-hoc scripts
- Promising methods that never become usable tools
Services offered
What we offer
Research software development
Reproducible pipelines, packages and tools with tests, documentation and version control.
Deep learning research solutions
Model design, training infrastructure and benchmarking for academic and industrial research.
Medical imaging & brain connectomics
Graph-based analysis of fMRI, DTI and structural MRI connectivity data, and MR spectroscopy workflows.
Scientific data analytics
Statistical analysis, visualisation and interpretable modelling of complex datasets.
Research automation tools
Automating data preparation, experiment tracking and literature workflows.
Academic technology consulting
Guidance on research computing, AI adoption and digital infrastructure for institutions.
Technology stack
Tools we use
- Python
- PyTorch
- PyTorch Geometric
- NumPy / SciPy
- Nilearn / MNE
- Jupyter
- LaTeX
- Git
- HPC & GPU workflows
Industry applications
Where it applies
- Universities
- Research labs
- Healthcare research
- Life sciences
Development approach
How we work on it
- Step 1
Understand the science
Work through the research question, data and constraints with your team.
- Step 2
Reproducible by design
Versioned data, configs and environments from the first commit.
- Step 3
Validate rigorously
Proper cross-validation, statistical testing and ablation studies.
- Step 4
Hand over cleanly
Documentation and training so your team owns the result.
Discuss your research computing project
Tell us about your goals and we will outline a practical first step.