Giulia Gronchi
Physicist • Ocean Modeler • Environmental Risk Analyst
PhD in Future Earth, Climate Change & Societal Challenges (University of Bologna); MS in Theoretical Physics (Sapienza Rome); CMCC researcher in environmental data engineering.
Specialized in active matter and stochastic physics applied to climate systems, ocean pollution modeling, and environmental risk assessment.
Proficient in scientific computing (Python, C, R), data engineering (SQL, dbt, Prefect), and visualization (matplotlib, plotly, Flourish, Power BI).
Education
- PhD, Future Earth & Climate Change — University of Bologna (Jan 2024)
- MSc, Theoretical Physics — Sapienza University of Rome (May 2020)
- BSc, Physics — Sapienza University of Rome (Mar 2017)
Professional Experience
Euro‑Mediterranean Center on Climate Change (CMCC)
Data Scientist – Climate Risk & Environmental Assessment
Feb 2024 – Feb 2025
- Engineered Python pipelines to ingest and harmonize data from Copernicus C3S, ECMWF, NASA, and NOAA into CMCC’s DSS.
- Co‑maintained CMS on GitHub for real‑time Lagrangian oil pollution modeling.
- Delivered analytical products and reports via Flourish, matplotlib, plotly, D3.js, and Power BI for policy stakeholders.
- Led internal training sessions in ocean modeling, scientific computing, and data quality standards.
Environmental Data Engineer & PhD Researcher
Nov 2020 – Jan 2024
- Defined requirements and architected an end-to-end ocean risk model; contributed to MonGOOS and OKEANOS frameworks.
- Processed and analyzed >1 TB of climate and ocean data, producing impact metrics and scientific insights.
- Presented webinars and led sessions at international conferences; served as Visiting Scholar at IHCantabria.
- Balanced independent research with collaborative roles across cross-functional teams.
Key Projects
Ocean Pollution Forecasting & Coastal Risk Assessment
- Implemented a two-stage Python framework for deep-ocean oil spill tracking, incorporating plume entrainment and intrusive dynamics.
- Presented findings at EGU 2022 and EGU 2024.
Micro-scale Heat Engine with Active Particles
- Developed a theoretical micro-scale thermal engine based on active matter principles and stochastic thermodynamics.
- Modeled colored noise persistence in microswimmers and assessed work extraction via bath parameter modulation—akin to a Stirling cycle.
- C-coded physics simulations; published in Physical Review E (2021 paper).
SQL-Based Analysis of GenDip Dataset
- Analyzed the GenDip dataset, covering ambassadorial gender representation (1968–2021).
- Employed PostgreSQL queries to extract spatial and temporal gender-gap trends.
- Shared results and methodologies via GitHub SQL analysis.
High-Resolution Bio-nanoparticle Characterization
- Deployed Laser Transmission Spectroscopy to quantify exosomes, bacteria, and cells in suspension.
- Designed a tunable-wavelength laser setup and developed a complementary software suite (Python, R, LabVIEW) during a 6‑month project at Sapienza.
Collaborations & Outreach
- Frequent invited speaker at CMCC seminars on environmental analytics and risk assessment.
- Mentored early‑career researchers in scientific programming and data best practices.
Let’s Connect
Open to partnerships in ocean modeling, environmental risk, and environmental data science: