We extract PhD project listings, funding structures, supervisor details, and institution profiles from FindAPhD. Delivered as clean JSON, CSV, or Parquet to S3, BigQuery, or Snowflake on your cadence.
Structured, schema-consistent data across all major object types — delivered clean, typed, and ready to query.
Complete list of extractable fields for Project Listings objects from findaphd.com. All fields typed and schema-versioned.
"project_id": "P89214", "title": "Machine Learning for Climate Modelling", "university": "University of Cambridge", "department": "Department of Computer Science and Technology", "supervisor": "Dr. Sarah Jenkins", "funding_type": "Funded PhD Project (Studentship)", "deadline": "2026-01-15T00:00:00Z", "location": "Cambridge, UK"
| # | project_id | title | university | department | supervisor | funding_type |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Funding Details objects from findaphd.com. All fields typed and schema-versioned.
"project_id": "P89214", "funding_status": "Fully Funded", "funding_amount": "19237.0", "eligibility": "UK/EU Students", "nationality_requirements": "UK, EU", "fee_status": "Home fees covered", "duration": "3.5 years", "stipend": "UKRI standard rate"
| # | project_id | funding_status | funding_amount | eligibility | nationality_requirements | fee_status |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Supervisor Profiles objects from findaphd.com. All fields typed and schema-versioned.
"supervisor_id": "S4219", "name": "Dr. Sarah Jenkins", "university": "University of Cambridge", "department": "Department of Computer Science and Technology", "research_interests": "['Machine Learning', 'Climate Science', 'Neural Networks']", "project_count": 3, "profile_url": "https://www.findaphd.com/supervisors/s4219/dr-sarah-jenkins"
| # | supervisor_id | name | university | department | research_interests | email_domain |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for University Data objects from findaphd.com. All fields typed and schema-versioned.
"university_id": "U112", "name": "University of Cambridge", "location": "Cambridge", "country": "United Kingdom", "department_count": 42, "total_projects": 318, "institution_type": "Public Research University"
| # | university_id | name | location | country | department_count | total_projects |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Search Results objects from findaphd.com. All fields typed and schema-versioned.
"keyword": "artificial intelligence", "position": 1, "project_id": "P89214", "title": "Machine Learning for Climate Modelling", "university": "University of Cambridge", "funding_badge": true, "featured_listing": false, "scraped_at": "2026-05-12T10:14:33Z"
| # | keyword | position | project_id | title | university | funding_badge |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Our FindAPhD scraper navigates complex taxonomy trees, extracts unstructured funding eligibility criteria, and maps supervisory networks across global institutions.
Title, description, entry requirements, deadlines, and project URLs — captured at the individual listing level.
Parse funding types, stipend amounts, fee coverage, and complex nationality eligibility rules from unstructured text.
Extract primary supervisors, co-supervisors, and their associated research interests across departments.
Map hierarchical data linking individual projects to specific research groups, departments, and universities.
Monitor rolling deadlines and fixed application cut-offs, timestamped per crawl for accurate historical analysis.
Extract subject tags, disciplinary categories, and search keywords to maintain accurate academic taxonomies.
Scrape listings across UK, Europe, North America, and Australasia, normalising location data and currency where visible.
Track when projects are filled or removed, maintaining a historical record of academic opportunities over time.
Run continuous pipelines at daily or weekly cadences with change-detection diffing to monitor the academic cycle.
Brief in. Clean data out.
Provide subject categories, university lists, or keyword sets. We design the extraction schema together.
We configure Scrapy crawlers, pagination logic, and parsing rules for FindAPhD's specific DOM structure.
Schema validation, null-rate checks, and funding eligibility normalisation before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Academic listings are notoriously unstructured. Here is how we ensure data quality and pipeline resilience.
FindAPhD categorises projects across thousands of nested subject nodes. Our crawler traverses this hierarchy systematically, ensuring comprehensive coverage without infinite loops or missed sub-disciplines.
Funding rules are often written as free text. We use regex and NLP heuristics to classify funding status into structured booleans (e.g., UK_eligible, EU_eligible, fully_funded) for downstream querying.
We handle dynamic pagination and result-limit constraints by slicing broad queries into granular search parameters, guaranteeing extraction of the entire project corpus.
For large university catalogues, we maintain a hash index of last-seen values per field. Subsequent runs only push diffs — reducing compute cost and downstream processing load.
Every run emits structured logs to our observability stack. We alert on null-rate spikes, schema drift, and coverage drops — and respond before you notice.
Universities benchmark their PhD offerings against peer institutions to identify gaps in research focus.
Higher education marketers analyse funding trends and project volumes to optimise recruitment campaigns.
Grant bodies and policy makers track the distribution of funded versus self-funded projects across scientific disciplines.
Educational portals aggregate project data to build specialised search engines for niche academic communities.
NLP teams use project descriptions and entry requirements to train models on academic terminology and research trends.
Research departments monitor competitor funding structures to design more attractive studentship packages.
"FindAPhD holds the most comprehensive registry of global academic research opportunities — but extracting structured funding and supervisory networks requires a dedicated pipeline."
Most teams underestimate the complexity of academic scraping: handling inconsistent department taxonomies, parsing unstructured funding eligibility, and managing pagination across thousands of subject nodes. DataFlirt abstracts this infrastructure so your data science team can focus on analysis, not HTML parsing.
Everything supported by our findaphd.com scraper — rendered SPA elements, auth walls, rate-limit evasion and beyond.
Open-source tooling on proven cloud infra — no vendor lock-in, full observability.
Scrapy handles crawl orchestration, deduplication, and retry logic. Playwright handles JavaScript rendering and interaction flows where necessary.
We maintain pools of proxies to ensure reliable access and prevent IP blocking during high-volume extractions.
Pipelines run on AWS Lambda and ECS. Airflow handles scheduling, dependency management, and SLA alerting.
Data delivered to where your team already works — no new tooling required.
About findaphd.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information from FindAPhD is generally permissible under applicable law. DataFlirt targets only public, non-authenticated project and university data. We do not extract personal user data or circumvent authentication walls.
Our selectors have multi-layer fallback chains. We monitor for null-rate spikes in real time and update parsing rules rapidly to ensure data continuity.
Full catalogue refreshes at weekly or daily cadences complete within defined SLA windows. Change detection ensures you only process new or updated listings.
Yes. Every pipeline run compares current listings against the historical database, flagging projects that have been removed or passed their deadline.
Our smallest packages start at a defined subset of disciplines or universities. For global extraction, we price based on volume and delivery frequency.
We extract contact information only when explicitly published on the public listing. We do not bypass contact forms or extract hidden data.
Yes. We provide a sample run of up to 500 projects as part of the pre-engagement scoping process to validate schema fit and data quality.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off database of engineering projects or a continuous feed of global funding opportunities — we scope, build, and operate the pipeline. Tell us what you need.
