The Computing Division at the NanoTRIZ Innovation Institute is a research-focused track for Fellows who want to build research-grade computational capability: rigorous problem framing, high-quality implementation, and evidence-based evaluation. It is not an accredited university department. It operates as a global, project-based mentorship ecosystem where supervisors and mentors are onboarded progressively.

Research focus and example topics

Projects in this Division span modern computing at the intersection of software engineering, data science, artificial intelligence, and computational research infrastructure. Typical directions include:

• Machine learning and applied AI for research workflows

• Information retrieval, natural language processing, and scientific search

• Research software engineering (reproducible pipelines, tools, and platforms)

• Optimization, simulation tooling, and benchmarking methodology

• Data processing, evaluation systems, and reliable experimentation

Mentorship model

Accepted Remote Fellows join from around the world and work on milestone-driven projects aligned with their background, readiness, and topic fit. When supervisors are available, Fellows are matched to a supervisor and contribute to research-grade outputs such as prototypes, controlled experiments, datasets or evaluation pipelines, technical reports, and publishable artifacts.

Research standards and ethical AI use

We treat code as a scientific instrument: correct by design, testable, documented, and reproducible. AI tools may be used to accelerate planning, drafting, refactoring, debugging support, and documentation, but the Fellow remains responsible for verification, correctness, and intellectual ownership. We emphasize safe and ethical AI use, including careful handling of data, privacy, and proper attribution.

What success looks like

The objective is not “learning to code” in isolation. The objective is the ability to design, build, and evaluate computational systems that can withstand scrutiny and support state-of-the-art research and innovation.

Pathways to join the Computing Division

Option A — Pre-Fellowship Preparation (recommended if you are not yet ready) 

Choose this route if you want to build the minimum research and engineering foundation before applying to the Fellowship. The preparation track helps you:

• define a clear research goal and project scope

• build a basic portfolio (GitHub, write-up, short demo)

• learn reproducible workflows (version control, documentation, evaluation)

• produce a “readiness package” for merit-based selection

Suggested Pre-Fellowship starting tasks (examples):

• Create a GitHub repository with a small, reproducible project (README + setup + results).

• Replicate one published baseline result or benchmark and report limitations.

• Build a simple evaluation pipeline (metrics + test cases + ablation plan).

• Write a 1–2 page research roadmap: problem → assumptions → methods → deliverables.

Outcome: you finish with artifacts that make your Fellowship application strong and verifiable.

Option B — Apply directly to the NanoTRIZ Innovation Fellowship

Choose this route if you already have evidence of readiness (projects, code portfolio, publications, or strong technical background) and you are ready to deliver measurable outputs within 6–12 months.

Strong signals for direct Fellowship entry:

• a public portfolio (GitHub / demos / technical write-ups)

• evidence of rigorous work (reproducibility, tests, evaluation, documentation)

• research outputs (preprints, posters, reports) or clear research plan

• ability to commit to milestone-driven work and reporting

What to include in your application (Computing Division)

To be evaluated on merit, submit:

• Portfolio links: GitHub / OSF / arXiv / project pages (required)

• Top 5 skills + evidence: each with a short proof link (required)

• Project proposal (1 page): problem, goal, method, milestones, risks

• Resources: tools/equipment access (compute, datasets, labs if any)

Example project proposals that fit this Division:

• retrieval/search system for scientific literature + evaluation pipeline

• NLP tool for structured literature mapping and claim tracing

• reproducible ML baseline replication + ablation study

• benchmarking framework for a computational method or dataset

• research-grade engineering of a prototype tool used in scientific workflows

Next step

If you are unsure which route fits you, start with Pre-Fellowship Preparation. If you already have strong evidence and a clear plan, apply directly to the Fellowship.