Services and Expertise

Core Service Areas

The service areas are designed for projects where standard data evaluation is not sufficient — for example when measurement signals are noisy, model assumptions matter, uncertainty needs to be quantified, or recurring analyses should be automated.

Signal Processing

Development and implementation of signal processing methods for extracting, characterizing, and interpreting information from measurement, sensor, and time-series data.

Typical tasks:

• Filtering and preprocessing of measurement signals
• Feature extraction from sensor and time-series data
• Spectral and frequency-domain analysis
• Time-series analysis and signal characterization
• Visualization of signal structures, trends, and patterns

Typical deliverables:

• Signal processing workflow
• Extracted signal features and result visualizations
• Documented preprocessing and analysis scripts

Modeling & Inference

Model-based and probabilistic methods for estimating unknown quantities, solving inverse problems, and deriving interpretable results from complex data.

Typical tasks:

• Parameter estimation
• Bayesian and probabilistic modeling
• Model-based Analyses
• Inverse problem formulation and solution
• Model interpretation and uncertainty evaluation

Typical deliverables:

• Implemented model or inference algorithm
• Parameter estimates with uncertainty evaluation
• Model diagnostics and interpretation summary

Statistical Analysis

Classical and Bayesian statistical modeling and evaluation of technical, scientific, and clinical datasets with transparent assumptions, quantitative results, and uncertainty-aware interpretation.

Typical tasks:

• Classical and Bayesian modeling
• Regression models and effect estimation
• Group comparisons and hypothesis testing
• Method comparison and validation
• Uncertainty quantification and performance metrics

Typical deliverables:

• Statistical analysis report
• Result tables, figures, and summary statistics
• Interpretation of statistical findings and uncertainty

Automated Workflows

Design and implementation of reproducible computational workflows, automated analysis pipelines, and project-specific tools in Python, MATLAB, or R.

Typical focus areas include:

• Automated data import and preprocessing
• Batch evaluation of repeated measurements or datasets
• Automated reporting and visualization workflows
• Development of project-specific analysis scripts
• Structuring of reproducible computational workflows

Typical outputs include:

• Reproducible Python, MATLAB, or R workflow
• Automated analysis or reporting pipeline
• Project-specific analysis tool or script package

Scientific Support

Analytical support for technical and scientific projects, including structured result presentation, method descriptions, documentation, and publication-ready outputs.

Typical focus areas include:

• Clarifying analytical objectives and reporting needs
• Structuring results for scientific communication
• Translating methods into clear written explanations
• Reviewing analyses, figures, and result sections
• Supporting revisions and responses to reviewers

Typical deliverables:

• Publication-ready figures and result tables
• Structured result summaries
• Method and analysis descriptions

How the Service Areas Work Together

How Projects Typically Work

A project usually starts with a short initial discussion to understand the data, the analytical objective, and the expected output.

Complex Data, Signals, or Models?