# University of Copenhagen PhD Jobs Scraper (KU) (`nomad-agent/math-ku-phd-scraper`) Actor Track open PhD and research positions at the University of Copenhagen's Department of Mathematical Sciences. Each record includes title, deadline and apply URL, straight from the department's employment feed — ideal for math/PhD alert bots and academic boards. - **URL**: https://apify.com/nomad-agent/math-ku-phd-scraper.md - **Developed by:** [Nomad.Dev](https://apify.com/nomad-agent) (community) - **Categories:** Jobs - **Stats:** 2 total users, 1 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 ## University of Copenhagen PhD Jobs Scraper (KU) Fetch all open PhD and research fellowship positions from the University of Copenhagen's HR employment feed — every faculty and department, not just one discipline. ### What University of Copenhagen PhD data does this scraper extract? Each result is one flat JSON record per job posting: | Field | Meaning | |---|---| | `id` | Stable source-side identifier | | `title` | Job title as posted | | `company` | Hiring institution (always "University of Copenhagen") | | `location` | Location / duty station | | `url` | Direct link to the posting | | `postedAt` | Publish date/time, ISO 8601 (`null` if the feed didn't provide one) | | `deadline` | Application deadline parsed from the posting text — ISO `YYYY-MM-DD` when the date format is recognised, the raw matched date text otherwise, or `null` when no deadline is stated | | `snippet` | Short plain-text description excerpt (only when `includeSnippet` is true) | ### How to scrape University of Copenhagen PhD with this Actor 1. Click **Try for free** / **Run** — no login to the target site, no cookies, no proxies to configure. 2. Adjust the input (`postedSince`, `maxItems`) or keep the defaults. 3. Run it and export the dataset as JSON, CSV or Excel, or read it over the [API](https://docs.apify.com/api/v2). Run it from your own code: ```python from apify_client import ApifyClient client = ApifyClient("") run = client.actor("nomad-jobs/math-ku-phd-scraper").call(run_input={"maxItems": 50}) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item["title"], "—", item["company"], item["url"]) ```` Or a single HTTP call that runs the Actor and returns items in one response: ```bash curl -X POST \ "https://api.apify.com/v2/acts/nomad-jobs~math-ku-phd-scraper/run-sync-get-dataset-items?token=" \ -H "Content-Type: application/json" \ -d '{"maxItems": 50}' ``` ### Input | Field | Type | Default | Notes | |---|---|---|---| | `maxItems` | integer | `25` | Maximum number of PhD openings to return (max `500`). Each result returned is billed. Set 0 for no limit (all current openings — typically 25-40 at any time). | | `includeSnippet` | boolean | `true` | Include a plain-text excerpt (up to 400 characters) from the job description in each result. | | `postedSince` | string (date) | *(none)* | Only return postings published on or after this date. Leave empty to return all currently open positions. | | `cacheTtlSeconds` | integer | `1800` | *(Advanced)* Reuse a previously fetched listing if it is younger than this many seconds, instead of fetching it again. Set 0 to always fetch fresh. | ### Output example ```json { "id": "164715", "title": "PhD fellowship in fault tolerant quantum algorithms", "company": "University of Copenhagen", "location": "Copenhagen, Denmark", "url": "https://employment.ku.dk/phd/?show=164715", "postedAt": "2026-06-28T00:00:00Z", "deadline": "2026-07-12", "snippet": "PhD fellowship in fault tolerant quantum algorithms PhD Project in state preparation, observable extraction or noise modelling Niels Bohr Institute Faculty of Science University of Copenhagen..." } ``` `deadline` is `null` for the small minority of postings whose text doesn't state an explicit date near the word "deadline". ### Pricing Pay per event: **$0.05 per Actor start** and **$0.004 per job returned**. 100 jobs ≈ $0.45. No subscription, no rental, no proxy costs — you pay only for what you fetch. ### Integrations Export the dataset as JSON, CSV or Excel from the Console, or read it programmatically over the [Apify API](https://docs.apify.com/api/v2) — including a single `run-sync-get-dataset-items` call (see below). Works with Make, Zapier and n8n via the Apify app/node, and is callable by AI agents through the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp). ### Use cases - PhD-alert bots for any discipline at the University of Copenhagen - Academic job aggregation for Scandinavia - Tracking Danish university hiring - Research-career services ### FAQ **Is it legal to scrape University of Copenhagen PhD?** This Actor reads only publicly available job postings — data any visitor can see without logging in. No personal data behind authentication is touched. Review the target site's terms and your local regulations for your specific use case. **Do I need an account on the target site?** No. Postings are fetched from public pages/APIs — no login, cookies or session tokens. **How fresh is the data?** Every run fetches live listings. Results are cached for `cacheTtlSeconds` (default 30 min, set 0 to always hit the source live). **How many jobs can I get?** `maxItems` caps the run (set 0 where supported for no cap). Most sources paginate from newest to oldest. **Something broken or missing?** Open an issue on the Actor's **Issues** tab — it is monitored and reliability fixes ship fast. ### Related Actors - [Research & Academic Jobs Scraper — 10 Sources](https://apify.com/nomad-jobs/researcher-bundle) - [EURAXESS Jobs Scraper — EU Research Positions](https://apify.com/nomad-jobs/euraxess-scraper) - [jobs.ac.uk Scraper — UK Academic & Research Jobs](https://apify.com/nomad-jobs/jobs-ac-uk-scraper) # Actor input Schema ## `maxItems` (type: `integer`): Maximum number of PhD openings to return. Each result returned is billed. Set 0 for no limit (all current openings — typically 25-40 at any time). ## `includeSnippet` (type: `boolean`): Include a plain-text excerpt (up to 400 characters) from the job description in each result. ## `postedSince` (type: `string`): Only return postings published on or after this date. Leave empty to return all currently open positions. ## `cacheTtlSeconds` (type: `integer`): Reuse a previously fetched listing if it is younger than this many seconds, instead of fetching it again. Speeds up back-to-back runs. Set 0 to always fetch fresh. ## Actor input object example ```json { "maxItems": 25, "includeSnippet": true, "postedSince": "", "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-agent/math-ku-phd-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-agent/math-ku-phd-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-agent/math-ku-phd-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=nomad-agent/math-ku-phd-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "University of Copenhagen PhD Jobs Scraper (KU)", "description": "Track open PhD and research positions at the University of Copenhagen's Department of Mathematical Sciences. Each record includes title, deadline and apply URL, straight from the department's employment feed — ideal for math/PhD alert bots and academic boards.", "version": "0.1", "x-build-id": "QrmHWmpxyjmdLTv1F" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/nomad-agent~math-ku-phd-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-nomad-agent-math-ku-phd-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/nomad-agent~math-ku-phd-scraper/runs": { "post": { "operationId": "runs-sync-nomad-agent-math-ku-phd-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/nomad-agent~math-ku-phd-scraper/run-sync": { "post": { "operationId": "run-sync-nomad-agent-math-ku-phd-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "maxItems": { "title": "Max items", "minimum": 0, "maximum": 500, "type": "integer", "description": "Maximum number of PhD openings to return. Each result returned is billed. Set 0 for no limit (all current openings — typically 25-40 at any time).", "default": 25 }, "includeSnippet": { "title": "Include snippet", "type": "boolean", "description": "Include a plain-text excerpt (up to 400 characters) from the job description in each result.", "default": true }, "postedSince": { "title": "Posted since", "type": "string", "description": "Only return postings published on or after this date. Leave empty to return all currently open positions.", "default": "" }, "cacheTtlSeconds": { "title": "Cache duration (seconds)", "minimum": 0, "type": "integer", "description": "Reuse a previously fetched listing if it is younger than this many seconds, instead of fetching it again. Speeds up back-to-back runs. Set 0 to always fetch fresh.", "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 } } } } } } } } } } ```