Research focus and example topics

Projects in this Division focus on the foundations that make research trustworthy and transferable across domains. Typical directions include:

• Modeling and simulation with validation logic (assumptions, calibration, sensitivity analysis)

• Experimental design and study protocols (variables, controls, confounders, auditability)

• Measurement science and uncertainty reasoning (calibration, error sources, confidence in results)

• Scientific instrumentation logic (measurement chains, signal quality, reliability and drift)

• Reproducible research infrastructure (analysis pipelines, benchmarking, documentation standards)

• Workflow engineering for research teams (versioning, traceability, and transparent reporting)

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 outputs such as: validated protocols, measurement plans, reproducible analysis pipelines, benchmarking frameworks, model validation reports, uncertainty analyses, tooling for research traceability, and publishable methods-focused artifacts.

Reproducibility standards and ethical AI use

We treat reproducibility as a scientific requirement, not an afterthought. Fellows learn to maintain research records, document procedures, separate exploratory work from confirmed findings, and report methods so others can repeat them. Where appropriate, Fellows adopt practical discipline such as benchmark testing, pre-registration logic, sensitivity analysis, uncertainty reporting, and transparent version control of code and data. AI tools may be used ethically to accelerate literature mapping, protocol drafting, code assistance, and structured documentation—but the Fellow remains responsible for verification, correctness, and intellectual ownership, with proper attribution and transparency.

What success looks like

The objective is not “learning tools.” The objective is the ability to produce credible, measurable, and reproducible results — and to build the methods and systems that help others do the same across scientific and engineering domains.

Pathways to join the Methods & Systems Division

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

Choose this route if you want to build a strong foundation before applying to the Fellowship. The preparation track helps you:

• design studies with clear variables, controls, and auditability

• build a basic portfolio (protocol, reproducible pipeline, validation note)

• learn workflow discipline (traceability, versioning, documentation standards)

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

Suggested Pre-Fellowship starting tasks (examples):

• Write a 1–2 page method protocol: variables → controls → measurement steps → confounders → acceptance criteria.

• Create a reproducible analysis pipeline (public dataset is fine): data → code → outputs → report.

• Build an uncertainty checklist: error sources, calibration needs, sensitivity to assumptions.

• Draft a model validation plan: assumptions, parameter ranges, benchmarks, and failure modes.

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

Option B — Apply directly to the NanoTRIZ Innovation Fellowship

Choose this route if you already have evidence of readiness (methods work, instrumentation, modeling, reproducibility artifacts) and you are ready to deliver measurable outputs within 6–12 months.

Strong signals for direct Fellowship entry:

• public method artifacts (protocols, pipelines, repos, reports, publications)

• evidence of rigor (validation logic, uncertainty reasoning, benchmarking, traceability)

• a realistic plan with milestones, risks, and evaluation criteria

• ability to commit to milestone-driven work and professional documentation

What to include in your application (Methods & Systems Division)

To be evaluated on merit, submit:

• Output links: OSF / GitHub / reports / publications / portfolio pages (required where available)

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

• Project proposal (1 page): method goal, protocol/validation plan, milestones, risks, acceptance criteria

• Resources: tools/equipment/computing access (if relevant)

Example project proposals that fit this Division:

• reproducible pipeline for a scientific task with benchmarks and traceable reporting

• experimental protocol redesign with controls, confounders, and acceptance criteria

• uncertainty and calibration framework for a measurement workflow or instrument

• simulation/model validation study with sensitivity analysis and defensible limits

• tooling to improve research traceability (templates, automation, reporting standards)