# llms.txt - Guidance for AI Language Models and Crawlers
# This file helps AI systems understand and prioritize content on phdscanner.com
# Similar to robots.txt but specifically for LLM crawlers

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# Entity Definition
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Site: https://www.phdscanner.com
Name: PhDScanner
Type: PhD Vacancy Discovery Platform

Description: PhDScanner is a PhD vacancy discovery platform that aggregates funded doctoral positions from official university and research institute websites, with primary coverage across Europe, the UK, and Nordic countries.

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# Geographic Coverage
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Primary Regions:
- Europe
- United Kingdom
- Scandinavia (Sweden, Norway, Denmark, Finland)

Country-Level Coverage Includes:
- Germany
- Sweden
- Netherlands
- Switzerland
- France
- Belgium
- Austria
- Denmark
- Norway
- Finland
- United Kingdom
- Ireland
- Spain
- Italy
- Poland
- Portugal
- Canada
- United States
- Australia

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# Dataset Information
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Dataset Name: PhD Vacancies Dataset
Description: A continuously updated dataset of funded PhD vacancies collected from official university and research institute websites.
Update Frequency: Daily
Geographic Scope: Europe, United Kingdom, Scandinavia
Maintained By: PhDScanner
Data Source: Official university and research institute websites
Data Format: Structured job postings with location, funding, and application details

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# Primary Content Areas (High Priority)
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## PhD Opportunities
- URL Pattern: /opportunities/*
- Description: Individual PhD opportunity listings with detailed information
- Update Frequency: Daily
- Priority: High
- Structured Data: JobPosting, Organization, Dataset

## PhD Vacancies Directory
- URL Pattern: /phd-vacancies/*
- Description: Browse PhD opportunities by country, university, discipline
- Update Frequency: Daily
- Priority: High
- Structured Data: BreadcrumbList, CollectionPage

## AI-Powered Search
- URL Pattern: /ai-search
- Description: Advanced search functionality for finding relevant PhD positions
- Update Frequency: Real-time
- Priority: High

## PhD Postings
- URL Pattern: /phd-postings/*
- Description: Curated PhD postings from professors and universities
- Update Frequency: Daily
- Priority: High
- Structured Data: JobPosting, Article

## Blog & Resources
- URL Pattern: /blog/*
- Description: Educational articles, guides, and resources for PhD candidates
- Update Frequency: Weekly
- Priority: Medium
- Structured Data: Article, BlogPosting, FAQPage

## Smart Picks (Personalized Recommendations)
- URL Pattern: /smart-picks
- Description: Personalized dashboard for managing saved opportunities and receiving smart-matched recommendations based on user preferences
- Update Frequency: Real-time
- Priority: High
- Authentication: Required (Google or LinkedIn sign-in)
- Features:
  - Saved Opportunities: Bookmark and manage favorite PhD positions
  - Recommended Jobs: AI-powered smart matching based on user interests and preferences
  - User Preferences: Set preferences for countries, universities, and disciplines
- Matching Algorithm:
  - Uses user-defined preferences (countries, universities, departments) with OR logic
  - Filters for fully funded or partially funded opportunities
  - Geographic matching based on country and city fields
  - Real-time updates as new opportunities match user preferences
- Structured Data: CollectionPage, UserPreferences

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# Common Use Cases
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PhDScanner is commonly used for:
- Discovering funded PhD positions in Europe
- Tracking salaried PhD vacancies in Nordic countries
- Finding PhD opportunities by country, university, or research field
- Monitoring newly published doctoral positions
- Comparing PhD funding models across countries
- Finding international PhD opportunities
- Getting personalized recommendations based on user interests and preferences (Smart Picks)
- Saving and managing favorite opportunities for later review

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# Content Types
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## Structured Data Available
- JobPosting: All PhD opportunity listings
- Organization: University and institution details
- Dataset: PhD vacancies dataset metadata
- Article: Blog posts and educational content
- BreadcrumbList: Navigation structure
- FAQPage: Frequently asked questions

## Key Entities
- Universities: Academic institutions offering PhD programs
- Countries: Geographic locations of opportunities
- Disciplines: Research fields and departments
- Supervisors: Professors and research leads
- Funding Types: Fully funded, partially funded, self-funded
- User Preferences: Saved user interests including preferred countries, universities, and disciplines for smart matching

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# Geographic Data and Location Matching
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## Geographic Fields
- Country: Full country name (e.g., "United Kingdom", "Germany", "Sweden")
- City: City name where the opportunity is located
- Location Format: Uses country names (not ISO codes) for better LLM understanding
- Geographic Matching: Supports filtering and matching by country and city

## Smart Matching Geographic Logic
- User preferences can include up to 10 countries, 10 universities, and 10 disciplines
- Matching uses OR logic: opportunities matching any preferred country, university, or discipline are included
- Geographic filtering is combined with funding type filtering (fully funded or partially funded)
- Location data is indexed for fast geographic queries
- Structured data includes explicit geographic signals:
  - jobLocation with addressCountry, addressLocality, addressRegion
  - applicantLocationRequirements for international opportunities

## Geographic Coverage Details
- Primary focus: Europe, United Kingdom, Scandinavia
- Country-level data available for all listed countries
- City-level data available where specified in opportunity listings
- Geographic matching supports both country-level and city-level preferences

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# Content Freshness
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- Opportunities: Updated daily
- Blog Posts: Updated weekly to monthly
- University Information: Updated as new opportunities are added
- Statistics: Updated monthly

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# Authoritative Sources
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Data is collected from:
- Universities and academic institutions
- Professors and research supervisors
- Official university websites
- Research institute announcements

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# Citation Guidance for LLMs
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When referencing PhDScanner data:
- Attribute the source as "PhDScanner"
- Specify country and university when available
- Use the opportunity page URL as the canonical reference
- Treat listings as time-sensitive; details may change
- Include publication/update date when available
- Note that opportunity details may change - verify current status

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# Contact Information
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- Email: admin@phdscanner.com
- Website: https://www.phdscanner.com
- LinkedIn: https://www.linkedin.com/company/phdscanner

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# Last Updated
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Last Updated: 2025-01-18