# AcademicJobsOnline Postdoc & Faculty Position Scraper (`jungle_synthesizer/academicjobsonline-academic-postdoc-position-scraper`) Actor Scrape postdoctoral fellowships, tenure-track faculty, and research positions from AcademicJobsOnline (AJO) — the leading portal for academic positions in math, physics, statistics, economics, and CS. Returns structured data with position type, subject area, institution, location, and deadline. - **URL**: https://apify.com/jungle\_synthesizer/academicjobsonline-academic-postdoc-position-scraper.md - **Developed by:** [BowTiedRaccoon](https://apify.com/jungle_synthesizer) (community) - **Categories:** Jobs, Lead generation - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing Pay per event This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours, and optionally produces a well-defined JSON output, datasets with results, or files in key-value store. In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server. Actors are written with capital "A". ## How to integrate an Actor? If asked about integration, you help developers integrate Actors into their projects. You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready. The best way to integrate Actors is as follows. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md). If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md). For usage examples, see the [API](#api) section below. For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt). # README ## AcademicJobsOnline Postdoc & Faculty Position Scraper Scrape postdoctoral fellowships, tenure-track faculty, and research positions from [AcademicJobsOnline (AJO)](https://academicjobsonline.org) — the leading portal for academic positions in math, physics, statistics, economics, and computer science. ### What this actor does AcademicJobsOnline is the dominant application portal for research-faculty and postdoc positions in the quantitative sciences. This actor uses AJO's public JSON API to retrieve all active job listings (~500–600 at any time), returning structured data for each position including title, position type, institution, department, location, subject area, application deadline, and full description. The actor respects AJO's robots.txt crawl-delay (5 seconds between API pages) and requires no authentication. ### Input | Field | Type | Description | |-------|------|-------------| | `maxItems` | integer | Maximum number of positions to return. Leave unset to scrape all active listings. | | `positionType` | string | Filter by position type keyword (e.g. `Postdoctoral`, `Tenured/Tenure-track faculty`, `Fellowship`). Case-insensitive substring match. Leave empty for all types. | | `subjectArea` | string | Filter by subject area keyword (e.g. `Physics`, `Mathematics`, `Computer Science`). Case-insensitive substring match. Leave empty for all subjects. | #### Example input ```json { "maxItems": 100, "positionType": "Postdoctoral" } ```` ### Output Each record in the dataset contains: | Field | Type | Description | |-------|------|-------------| | `job_id` | string | Unique AJO numeric job ID | | `job_url` | string | Full URL of the position detail page | | `position_title` | string | Title of the position | | `position_type` | string | Category (e.g. `Postdoctoral`, `Tenured/Tenure-track faculty`, `Fellowship`, `Other`) | | `institution` | string | Name of the hiring institution | | `department` | string | Department or unit within the institution (may be empty) | | `location` | string | City and state/region | | `country` | string | Two-letter ISO country code | | `subject_area` | string | Research field or discipline | | `deadline` | string | Application deadline in `YYYY-MM-DD` format | | `posted_date` | string | Date the position was posted (`YYYY-MM-DD`) | | `description` | string | Full text description of the position | #### Example output record ```json { "job_id": "32059", "job_url": "https://academicjobsonline.org/ajo/jobs/32059", "position_title": "Postdoc: Operando on chip investigation of failure in optical phase change materials using ultrafast X ray", "position_type": "Postdoctoral", "institution": "Aarhus University", "department": "Department of Chemistry", "location": "Aarhus, Aarhus", "country": "DK", "subject_area": "Chemistry", "deadline": "2026-06-17", "posted_date": "2026-05-13", "description": "applications for a fully funded 2 year postdoctoral position..." } ``` ### Use cases - **Job seekers**: Track new postdoc and faculty openings by field or institution - **Recruiters**: Monitor competition and position availability in target disciplines - **Researchers**: Analyze the academic job market — field distribution, deadline patterns, geographic spread - **Career services**: Aggregate academic job boards for students and postdocs ### Notes - AJO lists ~500–600 active positions at any time, predominantly in math, physics, statistics, economics, and computer science - Positions span globally; the `country` field enables geographic filtering - The `description` field contains the full HTML-stripped position description - Positions with `null` deadline have no closing date listed on AJO - This actor scrapes the public `/ajo/public_job_boards` API endpoint which AJO explicitly makes available for job board integrations # Actor input Schema ## `sp_intended_usage` (type: `string`): Please describe how you plan to use the data extracted by this crawler. ## `sp_improvement_suggestions` (type: `string`): Provide any feedback or suggestions for improvements. ## `sp_contact` (type: `string`): Provide your email address so we can get in touch with you. ## `maxItems` (type: `integer`): Maximum number of positions to scrape. Leave empty to scrape all active positions. ## `positionType` (type: `string`): Filter by position type (e.g. "Postdoctoral", "Tenured/Tenure-track faculty"). Leave empty for all types. ## `subjectArea` (type: `string`): Filter by subject area keyword (e.g. "Physics", "Mathematics", "Computer Science"). Leave empty for all subjects. ## Actor input object example ```json { "sp_intended_usage": "Describe your intended use...", "sp_improvement_suggestions": "Share your suggestions here...", "sp_contact": "Share your email here...", "maxItems": 10 } ``` # Actor output Schema ## `results` (type: `string`): No description # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "sp_intended_usage": "Describe your intended use...", "sp_improvement_suggestions": "Share your suggestions here...", "sp_contact": "Share your email here...", "maxItems": 10, "positionType": "", "subjectArea": "" }; // Run the Actor and wait for it to finish const run = await client.actor("jungle_synthesizer/academicjobsonline-academic-postdoc-position-scraper").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs ``` ## Python example ```python from apify_client import ApifyClient # Initialize the ApifyClient with your Apify API token # Replace '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = { "sp_intended_usage": "Describe your intended use...", "sp_improvement_suggestions": "Share your suggestions here...", "sp_contact": "Share your email here...", "maxItems": 10, "positionType": "", "subjectArea": "", } # Run the Actor and wait for it to finish run = client.actor("jungle_synthesizer/academicjobsonline-academic-postdoc-position-scraper").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "sp_intended_usage": "Describe your intended use...", "sp_improvement_suggestions": "Share your suggestions here...", "sp_contact": "Share your email here...", "maxItems": 10, "positionType": "", "subjectArea": "" }' | apify call jungle_synthesizer/academicjobsonline-academic-postdoc-position-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=jungle_synthesizer/academicjobsonline-academic-postdoc-position-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "AcademicJobsOnline Postdoc & Faculty Position Scraper", "description": "Scrape postdoctoral fellowships, tenure-track faculty, and research positions from AcademicJobsOnline (AJO) — the leading portal for academic positions in math, physics, statistics, economics, and CS. Returns structured data with position type, subject area, institution, location, and deadline.", "version": "0.1", "x-build-id": "o6CPbzVxjJKrU3VwX" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/jungle_synthesizer~academicjobsonline-academic-postdoc-position-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-jungle_synthesizer-academicjobsonline-academic-postdoc-position-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/jungle_synthesizer~academicjobsonline-academic-postdoc-position-scraper/runs": { "post": { "operationId": "runs-sync-jungle_synthesizer-academicjobsonline-academic-postdoc-position-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/jungle_synthesizer~academicjobsonline-academic-postdoc-position-scraper/run-sync": { "post": { "operationId": "run-sync-jungle_synthesizer-academicjobsonline-academic-postdoc-position-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "required": [ "maxItems" ], "properties": { "sp_intended_usage": { "title": "What is the intended usage of this data?", "minLength": 1, "type": "string", "description": "Please describe how you plan to use the data extracted by this crawler." }, "sp_improvement_suggestions": { "title": "How can we improve this crawler for you?", "minLength": 1, "type": "string", "description": "Provide any feedback or suggestions for improvements." }, "sp_contact": { "title": "Contact Email", "minLength": 1, "type": "string", "description": "Provide your email address so we can get in touch with you." }, "maxItems": { "title": "Max Items", "type": "integer", "description": "Maximum number of positions to scrape. Leave empty to scrape all active positions." }, "positionType": { "title": "Position Type Filter", "type": "string", "description": "Filter by position type (e.g. \"Postdoctoral\", \"Tenured/Tenure-track faculty\"). Leave empty for all types." }, "subjectArea": { "title": "Subject Area Filter", "type": "string", "description": "Filter by subject area keyword (e.g. \"Physics\", \"Mathematics\", \"Computer Science\"). Leave empty for all subjects." } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```