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MASOOD
SULTAN

Geoscientist & AI Engineer — Berlin · Open to PhD positions

Masood Sultan

My career didn’t begin in tech.
It began underground.

My foundation lies in interpreting the subsurface — 3D seismic surveys, geological models, wellbore data. I earned a BSc in Geophysics from Bahria University (3.55/4.00 CGPA) and placed 3rd in the Imperial Barrel Awards, Asia Pacific — one of the most competitive geoscience competitions organized by the AAPG, competing against teams from across the region.

I then moved to Berlin for an MSc in Global Change Geography at Humboldt University, where my focus shifted from subsurface to atmosphere — climate data, earth observation satellites, and computational modelling. When existing tools proved inadequate for the research questions I was asking, I taught myself to build them.

Today, my fieldwork is computational. I build LLM-powered systems and agentic architectures that crawl, extract, and adapt to complex environmental datasets — from real-time disaster APIs to satellite imagery. The geoscience training never left; it evolved into a different kind of excavation.

Technical Stack

Languages & Web

  • Python 3.x
  • TypeScript / Node.js
  • Express.js
  • HTML / CSS
  • R (RStudio)

AI & Automation

  • Agentic Systems (LLMs)
  • Open WebUI
  • Headless Web Scraping
  • Telegram Bots
  • Process Automation

Data & GeoScience

  • 3D Geomodelling (Petrel)
  • Spatial Analysis (QGIS)
  • Climate Data Analytics
  • Real-time APIs (FIRMS, USGS)
  • Pandas / NumPy

Infrastructure

  • Server-Sent Events (SSE)
  • REST API Architecture
  • Hugging Face Spaces
  • Docker / Appwrite
  • Git / PowerShell
01
TypeScriptNode.jsExpressSSE

TerraMind Core

A real-time global disaster intelligence platform entirely powered by open government APIs. Dynamically aggregates data from USGS Earthquakes, NASA EONET Wildfires, NOAA Weather Alerts, and NASA FIRMS Satellite Fire Detection into a unified stream. Features a built-in GeoScience AI Assistant.

4 Gov APIs UnifiedMulti-source normalizer pipeline
Server-Sent EventsReal-time push without polling
Interactive DashboardLive map markers + severity filters
View Terminal Output
02
PythonLLMsSelf-LearningMIT License

ai-scraper

A web scraper that teaches itself. Point it at any website — it figures out the structure, extracts data using LLMs, and remembers what worked. It builds domain-specific profiles, scores its own output quality, and auto-evolves its extraction prompts. Headless Chrome under the hood, with persistent memory that compounds over time.

Self-Evolving PromptsGets smarter with every scrape
Persistent MemoryDomain profiling compounds over time
Quality ScoringSelf-evaluates extraction accuracy
View Terminal Output
03
PythonTelegramAutomationAGPL-3.0

openhouse-bot

An adaptive, LLM-powered universal crawler that autonomously learns any website's structure — originally built to solve Berlin's housing crisis by scraping 50+ real estate portals worldwide. The underlying architecture is directly transferable to geoscience applications: scraping unstructured climate reports, policy documents, and global hydrological databases. Features real-time Telegram alerting and a Live Demo on Hugging Face.

50+ PortalsGlobal coverage, not just one platform
Instant Telegram AlertsNew listings hit your phone in seconds
Live HF DemoReal-time scraping in browser
View Terminal Output
Master’s Thesis 2025 Humboldt University of Berlin Global Change Geography

A Critical Review of Participatory Approaches in Water Management for Climate Change Adaptation

Supervisors: Prof. Dr. Tobias Krüger & Prof. Dr. Dieter Gerten · Geographisches Institut, Humboldt-Universität zu Berlin · 102 pages

Climate change is dismantling long-standing assumptions of stable hydrological patterns, exposing institutional fragmentation and amplifying inequality in water governance. This thesis develops the Conditional Enabling Framework (KIEA) — a diagnostic instrument that identifies when participation leads to effective, equitable, and adaptive water governance under climate stress.

Through a realist synthesis of 121 peer-reviewed documents (2009–2025), the research demonstrates that participation often devolves into tokenism or elite capture unless four interdependent enabling conditions are met simultaneously:

K — Knowledge PerformanceData availability, scientific literacy, and knowledge co-production among stakeholders
I — Institutional PerformanceGovernance capacity, regulatory design, and formal/informal institutional coherence
E — Equity PerformanceEquitable stakeholder representation, inclusive decision-making, and distributive justice
A — Adaptive PerformanceFlexibility, iterative learning, and system resilience under uncertainty

When any single dimension falls below threshold — the binding constraint — the entire governance system underperforms, regardless of strength in other areas. This weakest-link logic directly shifts theoretical climate governance toward measurable, actionable, and prescriptive implementation.

Journal of Applied Geophysics 2020 Elsevier 3D Geomodelling

A Case Study of 3D Geomodelling of Frontier Formation Second Wall Creek Sand, Teapot Dome, Wyoming, USA

Co-author · Khan, H.A., Sultan, M., Khan, M.J., Alvarez, M.D., Mehdi, S.D. & Javed, M.A.

An advanced 3D geomodelling workflow applied to the Teapot Dome anticline within the Powder River Basin — one of the largest producing basins in the United States, with 2.3 TCF gas and over 2.7 billion barrels of recoverable oil. Using integrated well log correlation, seismic interpretation, and stochastic property modelling in Schlumberger Petrel, the research demonstrates best practices in reservoir characterization for the Second Wall Creek Sand of the Frontier Formation.

Sultan, M. et al. (2020). Journal of Applied Geophysics, Vol. 179, 104114.

Bachelor’s Thesis 2018 Bahria University Petroleum Geoscience

3D Seismic Geomodelling of Cretaceous Shoreface Reservoir Sands, Frontier Formation, Teapot Dome, Wyoming, USA

BSc Geophysics · Bahria University, Karachi · 74 pages

The foundational geoscience research that seeded the Elsevier publication. This thesis focuses on the 3D seismic geomodelling of Cretaceous shoreface reservoir sands within the Teapot Dome — a publicly available dataset widely used in petroleum geoscience education. The work integrates seismic volume interpretation, well log data, stratigraphic correlation, and property modelling to build a comprehensive subsurface reservoir model.

Looking for a PhD position
in computational geoscience
or climate AI.

If you're seeking a researcher at the intersection of geoscience, AI, and climate adaptation — someone who can build the tools and do the science — let's connect. Based in Berlin.

Open to research positions
Download Academic CV →