Benjamin Roland

I am assisting the R&D team in designing and deploying a new electronic olfaction solution, integrating advanced Machine Learning techniques. This solution aims to replicate, through sensors and algorithms, the human ability to identify and differentiate complex odors in industrial contexts.

I participate in scoping the solution by defining technical objectives, business constraints, and work methodologies with the team. I help structure development steps, identify risks, and anticipate needs for data, instrumentation, and experimental validation.

I assisted the predictive modeling team in scoping a Machine learning solution using Open Data weather forecasts to predict frost risk on crops.

- Literature review.

- Determination of Key Performance Indicators and execution timeline for the Proof of Concept.

- Proposal of an architecture for industrialization.

I assisted the founding team in scoping a Machine learning solution using Open Data pollution forecasts to predict the impact on indoor air pollution.

- Review of functionalities and terms of use for the Copernicus Atmosphere Monitoring System API.

- Proposal of an execution plan combining preliminary feasibility study, Proof of Concept, and pilot project on customer data.

- Proposal of an architecture for the integration of the final solution into the client's AWS infrastructure.