Research services
- Python (NumPy, Pandas, Scikit-learn, TensorFlow)
- MATLAB/Simulink for system simulation
- R and RStudio for advanced statistical analysis
- C++, Java, JavaScript for software projects
- Complex systems simulation and mathematical modeling
- AI algorithms and machine learning
- Web and mobile application development
- Image processing and computer vision
Expected deliverables
- Agreed scope and work plan
- Editable files or code appropriate to the service
- Methods, assumptions and decision documentation
- Review notes and usage guidance
What to prepare
- Question, discipline and current research stage
- Institutional or journal requirements and deadline
- Existing manuscript or permitted de-identified data
- Expected deliverables and supervisor comments
Scope, fees, tools, timing, revisions and support are agreed in writing. Researchers retain responsibility for understanding and approving the work.
Our process
- Review your topic, degree, available data and deadline.
- Agree deliverables, timeline and a transparent quotation.
- Review milestone outputs and progress reports.
- Check methods, analysis, sources and writing quality.
- Receive editable files, code and agreed documentation.
Related guides
- Reproducible scientific code and simulation
- Research data management and reproducibility
- Statistical software compared: SPSS, R and Python
- Reproducible notebooks: Jupyter, R and execution environments
- A computational thesis with Quarto: data, figures, tables and text
- Research software papers and JOSS readiness