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P14 / education and leadership / deployed

NVIDIA Digital Twins, Simulation and Physical AI

Omniverse, Isaac Sim and Jetson integrated into engineering simulation, workshops, project supervision and academic-industry collaboration.

2025-2026NVIDIA Academic Programme, USA / UIDE, Ecuador
INTELLIGENT COMPUTATIONAL STACKNVIDIA Digital Twins, Simulation and Physical AI
01DATA SOURCEinstrument / sensor / simulation
02MODEL DESIGNfeatures / representations / data pipelines
03AI / ML / DLsupervised / unsupervised / NLP
04SCIENTIFIC CODEPython / MATLAB / CUDA / GPU
05DEPLOYembedded / full stack / digital twin / XR
06VALIDATEmetrics / experiments / real-case feedback

Engineering data moves through scientific computation, learning algorithms, validation and physical or digital deployment.

01

Problem or Industrial Need

Engineering education and prototype development needed a shared simulation-to-embedded stack for robotics, digital twins and AI-enabled systems.

02

Engineering or Scientific Solution

Deployment of NVIDIA Omniverse, Isaac Sim and Jetson across simulation, workshops, supervised projects and collaboration as the first official NVIDIA University Ambassador in Ecuador.

03

Sebastian's Technical Contribution

Introduced the platforms, developed learning and project workflows, supervised technical applications and led knowledge transfer.

04

Methods and Tools Used

  • Omniverse digital-twin workflows
  • Isaac Sim robotics simulation
  • Jetson embedded AI prototyping
  • Workshops and project-based technology transfer
05

Prototype, Simulation and Experimental Evidence

deployment

Three-platform stack

Omniverse, Isaac Sim and Jetson were used across four academic and engineering delivery areas.

06

Measurable Result or Published Finding

3

NVIDIA platforms deployed

Simulation, technical workshops, project supervision and academic-industry collaboration.

07

Diagrams and Publications

INTELLIGENT COMPUTATIONAL STACKNVIDIA Digital Twins, Simulation and Physical AI
01DATA SOURCEinstrument / sensor / simulation
02MODEL DESIGNfeatures / representations / data pipelines
03AI / ML / DLsupervised / unsupervised / NLP
04SCIENTIFIC CODEPython / MATLAB / CUDA / GPU
05DEPLOYembedded / full stack / digital twin / XR
06VALIDATEmetrics / experiments / real-case feedback

Engineering data moves through scientific computation, learning algorithms, validation and physical or digital deployment.

08

Role, Team Attribution, Institution and Project Context

First official NVIDIA University Ambassador in Ecuador; technical educator, project supervisor and academic-industry liaison with the NVIDIA Academic Programme, USA.

Connected work

P132024-2026

Multidisciplinary Mechatronics Project Development

Problem
Multidisciplinary student engineering requires a repeatable path from requirements and models to working prototypes and defensible validation.
Solution
A hybrid project-development framework combining conventional engineering control, Agile practices, technical gates and ABET-aligned outcomes.
Evidence / result
35 multidisciplinary projects coordinated
hybrid deliveryABETprototypingtechnical supervision
View system
P152020-Present

Smart Realities: Spatial Computing and Connected Systems

Problem
Emerging-technology concepts often fail to connect interactive software, physical hardware, learning content and a practical delivery strategy.
Solution
An independent R&D initiative integrating sensors, embedded systems, data processing, spatial interfaces and web-based engineering applications.
Evidence / result
4 cross-functional teams led
XR/ARIoTembedded systemsweb systems
View system
P232025

Autonomous Mobile-Robot Logistics

Problem
Autonomous logistics platforms require motion-planning methods that connect perception and computation with safe mobile-system behaviour.
Solution
A deep motion-planning research workflow developed around autonomous mobile-robot logistics.
Evidence / result
Accepted research output
mobile roboticsmotion planningdeep learningsimulation
View system