From operator to R&D lead. One curriculum.
Structured programs at every level. Multi-physics modeling, multi-scale simulation, process optimization, Studio operator workflows. Your team owns the predictive layer end-to-end, not just the dashboards on top of it.
A model nobody understands is a liability, not an asset.
Predictive models only deliver value when the people using them understand what they do, where they are reliable, and where to push back. Operators, process engineers and R&D leads all need that grounding. Without it, recommendations get ignored, calibrations drift, and the investment quietly stalls.
Our training programs remove that risk. Operators learn the Studio workflows that matter for their shift. Process engineers learn to interpret confidence intervals, recalibrate on new data and trigger reruns. R&D leads learn the underlying multi-physics and multi-scale modeling, so they can extend the platform to new chemistries on their own.
Thirteen years of computational modeling and AI, taught directly.
The curriculum is rooted in the same 13+ years of computational modeling and AI research from Prof. Franco's group that powers the Aikemics platform. Trainers are practising scientists and engineers, not external instructors reading from slide decks.
Course materials draw from peer-reviewed publications and validated case studies. Wherever possible, the exercises use your own line data, so the learning translates directly into operating practice instead of staying in the classroom.
What teams ask before booking.
Make sure the people running the model understand the model.
Tell us the team composition and the modules you are deploying. We return a proposed curriculum and a delivery plan within a week.