SUPERVISION OF DOCTORAL CANDIDATES
REALISE Main Supervisors
The REALISE main supervisors are internationally recognised researchers in igneous petrology, volcanology, critical raw materials, AI, and data science, providing scientific leadership and advanced training within the network.
EARLY CAREER RESEARCHERS
Doctoral Candidates and Doctoral Projects
The REALISE doctoral projects offer interdisciplinary PhD training at the interface of Earth Sciences and machine learning, with each Doctoral Candidate embedded in an international research environment supported by joint supervision and secondments.
Doctoral Candidate 1
Title: Deciphering the genesis of critical ore deposits in the Bushveld Layered Intrusions
Doctoral Candidate 2
Title: Decoding pre-eruptive dynamics at Campi Flegrei Caldera
Doctoral Candidate 3
Title: Machine Learning Assisted Atomistic Simulations to Unravel Element Partitioning of Critical Raw Elements in Magmatic Systems
Doctoral Candidate 4
Title: Generative AI Assisted super-resolution and artificial 3D tomography of crystal zoning patterns
Doctoral Candidate 5
Title: Data-Driven Investigations on the Evolution of Magma Plumbing Systems of Active Volcanoes
Doctoral Candidate 6
Title: Robust multi-phase machine learning thermo-chemo-barometry of volcano plumbing systems
Doctoral Candidate 7
Title: AI-assisted mineralogical and textural characterization of lithium-cesium-tantalum (LCT) pegmatites
Doctoral Candidate 8
Title: DeepEruptive: Artificial Intelligence for Eruptive Parameter Estimations
Doctoral Candidate 9
Title: Melt inclusions snapshot of pre-eruptive magma reservoir conditions
Doctoral Candidate 10
Title: Machine Learning assisted multi-modal investigations to unravel sulfide accumulation processes in ore deposits of economic interest
Doctoral Candidate 11
Title: AI supported hypothesis formulations on metal-rich granites
Doctoral Candidate 12
Title: Leveraging Traditional and Machine Learning Approaches for Petrological Monitoring and Eruptive Style Forecasting in Open Vent Volcanic Systems
Doctoral Candidate 13
Title: Advancing petro-volcanological hypothesis formulation and thermodynamic insights through Machine Learning
Doctoral Candidate 14
Title: DEGAS: ML-based atomistic simulations of volcanic degassing
Doctoral Candidate 15
Title: Large Language Models to Leverage Automated Data Explanations in Petrology
