# jobs.ac.uk Academic & Research Job Scraper (`nomad-dev/jobs-ac-uk-scraper`) Actor Get live academic and research job postings from jobs.ac.uk as clean structured data. Covers postdocs, lectureships, research fellowships, PhD studentships and professional roles across UK and European universities. Filter by keyword. No login required. - **URL**: https://apify.com/nomad-dev/jobs-ac-uk-scraper.md - **Developed by:** [Nomad.Dev](https://apify.com/nomad-dev) (community) - **Categories:** Jobs - **Stats:** 2 total users, 0 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing Pay per usage This Actor is paid per platform usage. The Actor is free to use, and you only pay for the Apify platform usage, which gets cheaper the higher subscription plan you have. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-usage ## 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 ## jobs.ac.uk Academic & Research Job Scraper Get live academic and research job postings from [jobs.ac.uk](https://www.jobs.ac.uk) — the UK's primary portal for university and research roles — as clean, structured data. **No login, no account, no setup required.** Monitor postdocs, lectureships, research fellowships, PhD studentships, teaching roles and professional services positions across UK and European universities. ### Why use it - **Always current** — reflects live postings, refreshed on every run. - **UK & EU academic coverage** — postdocs, lectureships, research fellows, studentships, professional services and more. - **Keyword fan-in** — search multiple disciplines in a single run; results are merged and deduped automatically. - **Clean structured output** — ready for spreadsheets, databases, dashboards or job-matching pipelines. ### What you get One dataset item per posting: | Field | Description | |---|---| | `jobId` | Stable jobs.ac.uk job identifier (e.g. `DRJ953`) | | `title` | Job title | | `company` | Hiring institution / employer | | `location` | Location as listed (defaults to `United Kingdom` when absent) | | `salary` | Salary band or range (where displayed) | | `postedAt` | Date the posting was placed (ISO `YYYY-MM-DD`) | | `url` | Direct link to the full job posting | ### Input | Field | Type | Default | Notes | |---|---|---|---| | `keywords` | array of strings | research defaults | Keyword queries to search. Each is searched separately; results are merged and deduped. Leave empty to use built-in research-oriented defaults. | | `maxItems` | integer | 25 | Maximum total postings to return across all keywords. `0` = no limit. | | `pageSize` | integer | 25 | Results requested per keyword query (portal max: 25). | #### Default keywords When `keywords` is left empty the scraper fans across these research-oriented terms: - `social science` - `politics` - `psychology` - `humanities` - `international development` - `research` #### Example input ```json { "keywords": ["machine learning", "bioinformatics", "epidemiology"], "maxItems": 50, "pageSize": 25 } ```` Returns up to 50 postings matching any of the three disciplines, newest first. ### Use cases - Academics and researchers tracking new positions before closing dates. - University HR and recruitment teams monitoring competitor postings. - Academic career coaches and career services offices. - Labor-market researchers studying higher-education hiring trends. - Job aggregators and newsletters covering the research and university sector. # Actor input Schema ## `keywords` (type: `array`): List of keyword queries to fan across. Each keyword is searched separately and results are merged (deduped by URL). Leave empty to use the built-in research-oriented defaults: social science, politics, psychology, humanities, international development, research. ## `maxItems` (type: `integer`): Maximum number of job postings to return across all keyword queries. Set to 0 for no limit. ## `pageSize` (type: `integer`): How many results to request per keyword search (portal maximum is 25). Increase if you are running with a large maxItems and few keywords. ## `cacheTtlSeconds` (type: `integer`): Cache the upstream fetch in the key-value store for this many seconds; re-runs within the window skip the network call. Set 0 to disable. ## Actor input object example ```json { "keywords": [ "social science", "psychology", "bioinformatics" ], "maxItems": 25, "pageSize": 25, "cacheTtlSeconds": 1800 } ``` # 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 = {}; // Run the Actor and wait for it to finish const run = await client.actor("nomad-dev/jobs-ac-uk-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 = {} # Run the Actor and wait for it to finish run = client.actor("nomad-dev/jobs-ac-uk-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 '{}' | apify call nomad-dev/jobs-ac-uk-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=nomad-dev/jobs-ac-uk-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "jobs.ac.uk Academic & Research Job Scraper", "description": "Get live academic and research job postings from jobs.ac.uk as clean structured data. Covers postdocs, lectureships, research fellowships, PhD studentships and professional roles across UK and European universities. Filter by keyword. No login required.", "version": "0.1", "x-build-id": "9CAdjB0MM4Epa9fK8" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/nomad-dev~jobs-ac-uk-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-nomad-dev-jobs-ac-uk-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/nomad-dev~jobs-ac-uk-scraper/runs": { "post": { "operationId": "runs-sync-nomad-dev-jobs-ac-uk-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/nomad-dev~jobs-ac-uk-scraper/run-sync": { "post": { "operationId": "run-sync-nomad-dev-jobs-ac-uk-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", "properties": { "keywords": { "title": "Keywords", "type": "array", "description": "List of keyword queries to fan across. Each keyword is searched separately and results are merged (deduped by URL). Leave empty to use the built-in research-oriented defaults: social science, politics, psychology, humanities, international development, research.", "items": { "type": "string" } }, "maxItems": { "title": "Max items", "minimum": 0, "type": "integer", "description": "Maximum number of job postings to return across all keyword queries. Set to 0 for no limit.", "default": 25 }, "pageSize": { "title": "Results per keyword query", "minimum": 1, "maximum": 25, "type": "integer", "description": "How many results to request per keyword search (portal maximum is 25). Increase if you are running with a large maxItems and few keywords.", "default": 25 }, "cacheTtlSeconds": { "title": "Cache TTL (seconds)", "minimum": 0, "type": "integer", "description": "Cache the upstream fetch in the key-value store for this many seconds; re-runs within the window skip the network call. Set 0 to disable.", "default": 1800 } } }, "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 } } } } } } } } } } ```