Go to nvidia.com/inception → “Apply Now” → select Academic Research as organization type. The form has ~10 fields. Paste the answers below directly. Approval typically comes within 3–5 business days.
Field 1
Organization name
Physical AI Lab Korea Use this consistently across all registrations. Short, searchable, English-friendly.
Field 2
Organization type
Academic Research Lab
Field 3
Website
[your lab site URL when live — use aaron.kr in the meantime]
Field 4 — most important
Describe your organization / research focus (2–4 sentences)
Physical AI Lab Korea is an independent academic research lab based in Daejeon, South Korea, focused on edge-deployed artificial intelligence for physical systems. Our work spans on-device computer vision, embedded model optimization, and the development of Physical AI curriculum for engineering universities. The lab is affiliated with multiple Korean universities and led by a researcher with a PhD in Information Communication Engineering and a publication record in CNN-based recognition, generative models for dataset augmentation, and AI in education. We are actively building a Jetson-based research infrastructure and publishing in KCI, Scopus, and IEEE-family venues.
Field 5
How are you using / planning to use NVIDIA technology?
We are deploying trained computer vision models onto NVIDIA Jetson hardware (Orin Nano and Jetson Nano) for edge inference benchmarking, sim-to-real gap analysis, and physical sensor pipelines. We use TensorRT for model optimization, JetPack SDK as our software foundation, and NGC pretrained models as baselines for transfer learning experiments. Student researchers are completing NVIDIA DLI certifications as part of lab onboarding. All resulting publications will acknowledge NVIDIA hardware and software.
Field 6
Stage of development
Early stage — lab founding, initial hardware acquisition, first research projects underway
Field 7
Primary use case / industry vertical
Robotics & Autonomous Systems / Education & Research
Field 8
Country
South Korea
What you get from Inception
- Hardware discounts - Up to 25% off Jetson developer kits from the NVIDIA store — useful if you buy before a grant arrives.
- Software & cloud access - NGC catalog, DLI course credits, and priority access to new NVIDIA software releases.
- Co-marketing eligibility - Lab can be listed in NVIDIA’s Inception directory — adds credibility to recruiting and grant applications.
- GTC access - Free or discounted passes to NVIDIA GTC conference — good for networking and a future poster presentation.
A few things that don’t fit neatly in the widget:
- On the Inception application: The single most important field is #4 — the research description. That’s where most labs get approved or rejected. The draft above leads with your PhD, your publication record, and your Jetson plans in that order, which is the right sequence for NVIDIA reviewers who care about credibility first, then technical fit.
- On the paper — one strategic move worth making now: Create the Google Sheet database today, even with just 10–15 entries, and share the link publicly. That way, when you submit in June, the sheet already has a creation date and edit history that predate submission — reviewers sometimes check this. It also lets you share it with students who can help populate it.
- On Section 4.4 specifically (Korea vs. global comparison): this is the section most likely to get quoted or cited by Korean policymakers and other journal authors. Write it carefully and diplomatically — acknowledge Korea’s strengths (semiconductor industry, STEM pipeline, government AI investment) before identifying the gaps. That framing will land better with Korean reviewers and readers.
- On the 3×3 curriculum framework in Section 5.4: I’d recommend naming it something short and citable — even something like the “PAI-9 Framework” (Physical AI, 9 competencies). Named frameworks get cited far more than unnamed matrices. It also gives the lab an intellectual contribution that isn’t just a survey.