Daniel Sánchez-Gómez

Runs the full research lifecycle of data-driven projects, including data acquisition, model training, and delivery. Built VORTEX, a Random Forest provenance framework with 95% accuracy, and maintains open-source software and datasets.

Developed a research project within a network-science group using advanced computational methods. Acquired, curated, and processed network data and presented progress in bi-weekly reviews.

Developed MACLAS, an open-source supervised multiclass framework for mineral classification, and co-developed PEPAdb, an open relational database. Produced analytical datasets and disseminated results at international conferences.

Conducted field surveys to locate amber-bearing deposits and collect geological samples. Wrote technical reports and scientific publications and managed project communication channels.

Performed field data acquisition including GPS and landscape survey, site recording, and sample collection. Handled data entry and quality control in the project database and supported excavation planning.

Designed and ran experiments with archaeometric data, acquiring, processing, and analyzing measurements. Developed machine-learning models on archaeometric data and contributed to scientific reports and publications.

Planned and managed excavation and survey projects, leading field teams and handling equipment logistics, GPS surveying, sample collection, and database recording. Wrote technical reports and delivered them to clients and regulators on schedule.

Pursuing a PhD in Archaeology with a focus on computational archaeology and personal adornment in the Iberian Peninsula.

Completed specialised training in ABM and Network Science in Archaeology.

Completed specialised training in Agent-Based Modeling.

Grade: 8.78/10

Completed a Master's in Archaeology with honours, applying machine learning techniques to archaeological pottery classification.

Earned a Bachelor's degree in Anthropology.