Nanotechnology Engineering Technologists and Technicians
Architecture and Engineering Occupations- O*NET-SOC コード
- 17-3026.01
Implement production processes and operate commercial-scale production equipment to produce, test, or modify materials, devices, or systems of unique molecular or macromolecular composition. Operate advanced microscopy equipment to manipulate nanoscale objects. Work under the supervision of nanoengineering staff.
職業名とタスク記述は、公表された英語のまま表示します。ラベル（タスクの種類を含む）は翻訳しています。
AI露出度
- データ出典: BLS公表時点: 2026-08
高い· 相対
低い4段階の相対区分非常に高い職業群単位の値
尺度・母数・出典
4段階の相対区分（低い / 中程度 / 高い / 非常に高い）
BLS雇用見通し表の詳細職業831件が母数。値は NEM(全国雇用マトリクス)コード単位で付与されるため、同じ NEM コードの職業は同じバンドになります
- データ出典: Anthropic公表時点: 2026-03
0.000
0.000ここに掲載された値の範囲0.745職業群単位の値
尺度・母数・出典
観測エクスポージャー指数、公開されたまま0–1
O*NETタスクへの対応づけが基準
SOC 2018の職業単位で公表された値で、同じSOC 2018コードを持つO*NET職業はすべてこの値になります
- データ出典: ILO公表時点: 2025
0.26
0.09ここに掲載された値の範囲0.70職業群単位の値
尺度・母数・出典
生成AI露出度指数、公開されたまま0–1
ISCO-08の細分類（4桁）単位で公表された値です。米国労働統計局（BLS）の公式対応表（ISCO-08→2010 SOC、2010 SOC→2018 SOC）を公表どおり適用してこの職業に結び付けており、対応は全部または一部です
この出典がどのような性格の値か
BLSの区分は絶対水準ではなく相対順位であり、一次測定でもありません。複数の既存研究が付けた職業別パーセンタイル順位を4段階にまとめた値です。雇用や賃金の予測でもなく、導入確率でもなく、自動化と増強を区別しません。
AI 曝露度（OpenAI ルーブリック）
評価済みタスク 25 件 · β ≥ 0.5 のタスク 14 件（56.0%）
β = 直接的な露出（E1）+ 0.5 × ツール利用時の露出（E2）。原典リポジトリの定義に従います。
- 原典の単位：O*NET 27.2 のタスク → O*NET 31.0 の職業コード
- 評価済みタスクはすべて O*NET 31.0 のタスク一覧にあります。
- 出典
- OpenAI "GPTs are GPTs" exposure rubric
- 版
- gh-main-0471612
- ライセンス
- MIT License, Copyright (c) 2024 OpenAI
タスク
O*NET® 31.0 Database のタスク記述です。コアタスクを先に表示します。
| タスク | 種類 | β（OpenAI） |
|---|---|---|
| Operate nanotechnology compounding, testing, processing, or production equipment in accordance with appropriate standard operating procedures, good manufacturing practices, hazardous material restrictions, or health and safety requirements. | コア | 0 |
| Maintain work area according to cleanroom or other processing standards. | コア | 0 |
| Produce images or measurements, using tools or techniques such as atomic force microscopy, scanning electron microscopy, optical microscopy, particle size analysis, or zeta potential analysis. | コア | 0.5 |
| Collaborate with scientists or engineers to design or conduct experiments for the development of nanotechnology materials, components, devices, or systems. | コア | 0.5 |
| Repair nanotechnology processing or testing equipment or submit work orders for equipment repair. | コア | 0 |
| Measure or mix chemicals or compounds in accordance with detailed instructions or formulas. | コア | 0 |
| Monitor equipment during operation to ensure adherence to specifications for characteristics such as pressure, temperature, or flow. | コア | 0 |
| Collect or compile nanotechnology research or engineering data. | コア | 0.5 |
| Calibrate nanotechnology equipment, such as weighing, testing, or production equipment. | コア | 0 |
| Monitor hazardous waste cleanup procedures to ensure proper application of nanocomposites or accomplishment of objectives. | コア | 0 |
| Contribute written material or data for grant or patent applications. | コア | 1 |
| Inspect or measure thin films of carbon nanotubes, polymers, or inorganic coatings, using a variety of techniques or analytical tools. | コア | 0.5 |
| Develop or modify wet chemical or industrial laboratory experimental techniques for nanoscale use. | コア | 1 |
| Implement new or enhanced methods or processes for the processing, testing, or manufacture of nanotechnology materials or products. | コア | 0.5 |
| Prepare detailed verbal or written presentations for scientists, engineers, project managers, or upper management. | コア | 1 |
| Maintain accurate record or batch-record documentation of nanoproduction. | コア | 1 |
| Prepare capability data, training materials, or other documentation for transfer of processes to production. | コア | 1 |
| Assist nanoscientists or engineers in processing or characterizing materials according to physical or chemical properties. | コア | 0.5 |
| Assist nanoscientists or engineers in writing process specifications or documentation. | コア | 1 |
| Compare the performance or environmental impact of nanomaterials by nanoparticle size, shape, or organization. | 補足 | 0.5 |
| Process nanoparticles or nanostructures, using technologies such as ultraviolet radiation, microwave energy, or catalysis. | 補足 | 0 |
| Perform functional tests of nano-enhanced assemblies, components, or systems, using equipment such as torque gauges or conductivity meters. | 補足 | 0 |
| Assemble components, using techniques such as interference fitting, solvent bonding, adhesive bonding, heat sealing, or ultrasonic welding. | 補足 | 0 |
| Analyze the life cycle of nanomaterials or nano-enabled products to determine environmental impact. | 補足 | 0.5 |
| Measure emission of nanodust or nanoparticles during nanocomposite or other nano-scale production processes, using systems such as aerosol detection systems. | 補足 | 0 |
職業情報
出典と帰属表示
This page includes information from the O*NET® 31.0 Database (https://www.onetcenter.org/database.html) by the U.S. Department of Labor, Employment and Training Administration (USDOL/ETA). Used under the CC BY 4.0 license (https://creativecommons.org/licenses/by/4.0/). O*NET® is a trademark of USDOL/ETA. AI Changing Work has modified all or some of this information: the O*NET-SOC code, title and task statements are reproduced in English without change; task-type labels are shown in the page's language and tasks are listed core first; any Korean occupation title shown on the Korean-language page is AI Changing Work's translation; any KSCO-8 unit groups linked to this occupation were paired with it by AI Changing Work's judgment, and the relation labels and statuses are AI Changing Work's additions. USDOL/ETA has not approved, endorsed, or tested these modifications.
Any AI exposure figures on this page are published by third parties, not by AI Changing Work, and none is part of the O*NET information. OpenAI publishes task-level scores (MIT License) for O*NET 27.2 task statements; each is shown next to the O*NET 31.0 task statement with the same task ID, whose wording can differ from the 27.2 statement that was scored. OpenAI also publishes occupation-level scores for O*NET-SOC codes in the same release, and any such score is shown on the O*NET occupation with the same code. Anthropic publishes an observed exposure index in the Anthropic Economic Index (CC-BY), and the U.S. Bureau of Labor Statistics publishes relative AI exposure categories (public domain); both are published per SOC code, and each value is shown on every O*NET occupation with that code. The International Labour Organization publishes a generative AI exposure index in ILO Working Paper 140 (CC BY 4.0) for ISCO-08 unit groups; AI Changing Work links those groups to O*NET occupations by applying the U.S. Bureau of Labor Statistics ISCO-08 to 2010 SOC and 2010 SOC to 2018 SOC crosswalks as published, without case-by-case selection, and these crosswalks match many groups only in part. Where several unit groups are linked, each group's published value is listed, and any summary shows only the lowest and highest of those values with the number of groups; no exposure figure is averaged or recalculated. Any employment figures are published by the U.S. Bureau of Labor Statistics for the SOC group containing this occupation. Each source is credited where its figures are shown.
O*NET OnLine: 17-3026.01 Nanotechnology Engineering Technologists and Technicians
KSCO 코드·명칭: 한국표준직업분류(제8차 개정) — 통계청 고시 제2024-328호 (2024-07-01 고시, 2025-01-01 시행). 저작권법 제7조 제2호의 고시 항목이다. 명칭 표기(가운뎃점·띄어쓰기)는 해설서 2차 정오 반영판의 표기를 따랐으며, 고시 항목표와는 18개 명칭에서 가운뎃점 글리프나 띄어쓰기만 다르다. 통계청은 2025년 10월 국가데이터처로 개편되었다. 이 페이지의 KSCO 연결은 통계청·국가데이터처의 공식 연계표가 아니다.