We support R&D teams facing complex analytical or computational challenges by delivering models, algorithms, and methods built for accuracy and robustness under real‑world constraints. As a technical partner, we combine analytical rigor with pragmatic implementation to frame difficult problems early and develop solutions that remain reliable in demanding operational settings.
Areas of expertise
Physical system modeling
We develop analytical and numerical models of mechanical, acoustic, and multi‑physics systems to guide design decisions, predict performance and validate concepts.
Our work supports applications such as precision watch components, acoustic detection systems, advanced sensing architectures and other complex dynamical mechanisms.
Machine learning and data-driven analysis
We build forecasting, classification and decision‑support methods that remain robust under operational conditions.
Our experience includes demand prediction, particle characterization, anomaly detection and probabilistic modeling for high‑stakes decision processes.
Quantum technologies
We assist teams exploring quantum computation and communication by providing algorithmic expertise, hardware‑level insight, and architectural modeling.
Our work spans algorithm execution on quantum hardware, network‑level analysis and computational methods for emerging quantum architectures.
Selected projects
A selection of public examples illustrating the range of technical projects we work on.
Modeling · Watchmaking
Balance spring for a timepiece resonator
We design balance‑spring geometries with tuned thickness and pitch profiles to reduce rate variations.
Signal processing · Acoustics
Detection of transient shockwave signatures
We develop signal‑processing methods that identify transients associated with ballistic events.
Machine Learning · Classification
Characterizing non‑exhaust particulate matter in road dust
We integrate SEM/EDX analysis with automated classification to conduct source apportionment.
Machine Learning · Forecasting
Predicting outcomes in Swiss popular votes
We integrate media, party and polling signals into machine‑learning models to predict collective decision outcomes.
Quantum computing · Optimization
Analog counterdiabatic quantum computing
We build computational and control techniques that advance the capabilities of analog quantum devices.
About us
Gradiom builds on a strong scientific background and several years of industrial R&D experience in high‑precision engineering. This combination of academic training and practical exposure shapes how we approach complex technical problems. While much of our work is carried out under confidentiality, selected publications and public projects illustrate the standards we apply to modeling, algorithm design and system analysis. We welcome conversations with teams exploring new challenges and would be glad to connect via LinkedIn for an initial exchange.