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General Information

Full Name Robin Thibaut, PhD
ORCID 0000-0001-7556-2700
Languages English (Fluent), French (Fluent), Dutch (Intermediate), Vietnamese (Intermediate)
Location Salt Lake City, UT, USA

Core Skills

  • Programming & ML
    • Python (NumPy, Pandas, scikit-learn, PyTorch, TensorFlow)
    • Uncertainty Quantification (Bayesian, BEL)
    • Experimental Design, Physics-informed ML
    • Time-series & Spatial Modeling
  • Geoscience Modeling
    • Finite-element thermal simulation
    • MODFLOW, MT3DMS, MODPATH, ModelMuse
    • CRTOMO, RES2DINV, SGeMS
    • Geophysical Data Integration (ERT/IP)
    • Hydrologic & Groundwater Modeling
  • Data & Engineering
    • SQL, Snowflake, Git
    • ETL/Data Engineering
    • Cloud (Google Cloud Platform), CI/CD
    • Scientific Visualization
    • Linux/macOS/Windows

Experience

  • Oct 2024 - Present
    Computational Geoscientist
    Zanskar Geothermal & Minerals, USA
    • Prioritize drilling targets by fusing finite-element thermal simulations with ML ranking to reduce prospect uncertainty and raise decision confidence
    • Optimize well placement via multi-objective search (thermal drawdown, flow, risk) and data-driven experimental design; deliver prioritized drilling recommendations with techno-economic screening
    • Calibrate 3D finite-element thermal models to well logs and geophysics; history match field observations and produce P10/P50/P90 temperature/flow forecasts for decision support
    • Technical lead for the geothermal modeling codebase: own internal repositories, establish code review, unit tests, and CI/CD; manage releases to ensure reproducible modeling across teams
    • Operationalize data flows (Python, SQL/Snowflake, Git, GCP) with versioned ETL for site and simulation data, standardizing provenance and enabling dependable reruns
    • Ship decision-ready maps and briefs to subsurface teams and leadership, streamlining cross-functional reviews and recommendations
  • Aug 2023 - Sep 2024
    Postdoctoral Researcher
    Lawrence Berkeley National Laboratory, USA
    • Integrated geophysical, hydrologic, satellite, and field data into spatiotemporal ML models to predict watershed function under climate scenarios
    • Built Python pipelines (Pandas/NumPy/scikit-learn/PyTorch) for feature engineering and cross-validation; developed reproducible workflows for predictive modeling
    • Co-authored model–data integration work on snowmelt/mountain hydrology (Water Resources Research, under review); contributed to data collection and analysis
  • Mar 2023 - May 2023
    Postdoctoral Researcher
    Ghent University (TURBEAMS), Belgium
    • Predicted turbidity/SPM from multibeam backscatter via deep learning and point-cloud analytics for water-quality monitoring
    • Scaled data handling to millions of points per session, reducing training wall-time
  • Mar 2019 - Mar 2023
    PhD Fellow
    Ghent University, Belgium
    • Created Bayesian Evidential Learning framework for experimental design, selecting measurements that minimize posterior uncertainty and reduce required simulations
    • Compared wells vs. geophysics for temperature monitoring within a BEL design framework (WRR 2022), quantifying information-gain vs. cost trade-offs
    • Published peer-reviewed articles and presented at international conferences; maintained open-source tools (SKBEL, pysgems)
  • Mar 2018 - Jan 2019
    Project Engineer
    G-tec, Belgium
    • Executed marine geophysical surveys (UXO detection, stratigraphy) with QA/QC; delivered coverage on budget and schedule
    • Coordinated mobilization and HSE compliance, maintaining an exemplary safety record and improving operational efficiency

Education

  • 2019 - 2023
    Ph.D. in Geological Sciences
    Ghent University, Belgium
    • Laboratory for Applied Geology and Hydrogeology
  • 2014 - 2017
    M.Sc. in Geological Engineering
    University of Liège, Belgium
    • Cum Laude
  • 2010 - 2014
    B.Sc. in Geological Sciences
    Free University of Brussels, Belgium
    • Cum Laude

Selected Public Projects

  • Pennsylvania, USA (2023–ongoing)
    Rare Earth Elements — Multiphysics AI-aided Autonomous Prospecting (REE-MAP)
    • Conducted geophysical data collection, processing, inversion, and interpretation in a multiphysics, AI-aided approach to identify REE–CM "hot zones" in coal tailings; field-tested at multiple sites
    • GSA Connects 2024 abstract with authorship credit and DOI: 10.1130/abs/2024AM-404345
  • Belgium (project 2021–2026; contributor 2023)
    TURBEAMS — Towards 3D turbidity by correlating multibeam sonar and in-situ sensor data
    • Built deep-learning models linking multibeam water-column backscatter with turbidity/SPM; contributed Python data flows toward 3D turbidity/SPM imaging plus a new processing library and workflows
    • National research program (New RV Belgica, BELSPO) with published ML objectives (incl. BEL) for large multibeam/optical datasets
  • Vietnam (2019–ongoing)
    Imaging Saltwater Intrusion, Luy River Coastal Aquifer (Vietnam)
    • Co-authored an open-access ERT study delineating the saline boundary; 21 ERT profiles revealed a larger intrusion zone than previously recognized
    • Water 2021 (MDPI), DOI: 10.3390/w13131743
  • Belgium (2019–2023)
    Bayesian Evidential Learning (BEL) for Experimental Design in Earth Sciences
    • Advanced BEL-based experimental design to minimize posterior uncertainty and improve monitoring design; resulted in peer-reviewed publications and the SKBEL open-source package
    • Journal of Hydrology 2021, 10.1016/j.jhydrol.2021.126903; Water Resources Research 2022, 10.1029/2022WR033045

Open Source Software & Datasets

  • 2019 - present
    SKBEL
    • Bayesian Evidential Learning framework built on top of scikit-learn
    • DOI: 10.5281/zenodo.6205242
  • 2021
    MGS-public
    • Minimum Gradient Support inversion for resistivity/IP data
  • 2024
    Zanskar's GeoGym
    • Benchmark dataset for evaluating geothermal exploration strategies
    • Published with Stanford Geothermal Workshop 2025 paper
  • 2021
    pysgems
    • Use SGeMS (Stanford Geostatistical Modeling Software) within Python
    • DOI: 10.5281/zenodo.4773587

Peer Review & Editorial Service

  • Reviewer (2021–2024): Journal of Hydrology (4), Geophysical Journal International (3), GEOPHYSICS (3), Geophysical Research Letters (2), Advances in Water Resources (4), Pure and Applied Geophysics (2), Hydrogeology Journal (1), Water Resources Research (1), GEUS Bulletin (1)

Teaching

  • 2019 - 2023
    Teaching Assistant
    Ghent University
    • Groundwater Modeling course
  • Mar 2022
    Workshop Organizer and Speaker
    Ghent University
    • Python Workshop
  • 2016 - 2019
    Private Tutor
    • Mathematics and Physics for university students