Doctoral Student

Applications for 15 Doctoral Candidate positions within the REALISE – MSCA Doctoral Network are open!

If you want to become a next-generation Earth scientist and entrepreneur, capable of developing innovative strategies for petrological data collection, modeling, and interpretation, this is the right time to apply!

We are looking for highly motivated and curious candidates ready to get involved!

More info can be found in our official website

In the recruitment section

REALISE - Bridging Igneous Petrology and Machine Learning for Science and Society

REALISE will train 15 Doctoral Candidates at the interface of igneous petrology, volcanology, critical raw materials, and machine learning / AI. The network combines advanced petrological observations and multimodal analytical data with modern ML (including physics-informed and generative AI approaches) to improve:

• Volcanic hazard assessment and risk mitigation, and
• The understanding and exploration of critical raw materials in magmatic systems.

Doctoral projects address magma lifecycle processes, multimodal data fusion, physics–ML hybrid modelling (from CFD to atomistic simulations), and AI‑assisted hypothesis formulation.

Each position is fully funded 36‑month PhD fellowship within the MSCA framework and includes international secondments, advanced training, and competitive employment conditions according to MSCA rules.

MSCA Doctoral Candidate eligibility criteria

Applicants must comply with the Marie Skłodowska‑Curie Doctoral Network eligibility rules, including:

• Applicants must not hold a PhD degree at the time of recruitment.
• At the time of recruitment, the applicant must not have resided or carried out their main activity (work, studies, etc.) in the host country for more than 12 months in the 36 months prior to the recruitment date.

DC1– Deciphering the genesis of critical ore deposits in the Bushveld Layered Intrusions

Host/PhD: University of Perugia (Italy) | Supervisor: Prof. Maurizio Petrelli

DC2– Pre‑eruptive dynamics at Campi Flegrei: multimodal petrology + physics‑informed AI

Host/PhD: University of Perugia (Italy) | Supervisor: Prof. Maurizio Petrelli

DC3– ML‑assisted atomistic simulations: partitioning of critical raw elements in magmas

Host/PhD: University of Perugia (Italy) | Supervisor: Prof. Maurizio Petrelli

DC4– Generative AI for super‑resolution & artificial 3D tomography of crystal zoning

Host/PhD: Leibniz University of Hannover (Germany) | Supervisor: Prof. Monika Sester

DC5– Data‑driven investigations of magma plumbing systems at active volcanoes

DC6– Multi‑phase ML thermo‑chemo‑barometry of volcano plumbing systems

DC7– AI‑assisted mineralogical/textural characterization of LCT pegmatites

DC8– DeepEruptive: AI for eruptive parameter estimations (petrology + CFD + tephra maps)

DC9– Melt inclusions: ML analysis of hyperspectral data for magma reservoir conditions

Host/PhD: CNRS/University of Orleans (France) | Supervisor: Dr. Giada Iacono‑Marziano

DC11– AI‑supported hypothesis formulation on metal‑rich granites (critical metal prospectivity)

Host/PhD: CNRS/University of Orleans (France) | Supervisor: Dr. Fabrice Gaillard

DC13– ML for hypothesis formulation + trace‑element thermodynamic insights in silicic systems

DC14– DEGAS: ML‑based atomistic simulations of volcanic degassing

DC15– Large Language Models for automated data explanations in petrology (incl. multimodal fusion)

Host/PhD: University of Ljubljana (Slovenia) | Supervisor: Prof. Blaž Zupan

First Call Application deadline: 31 March 2026

Remuneration: The remuneration is based on the MSCA‑DN researcher allowances (Living allowance: 4,010 € (adjusted by country coefficient); mobility allowance: 710 €; family allowance: 660 € (if eligible). The exact gross salary differs depending on the country and host institution. Full details are provided in the detailed descriptions of each position on the project website or during the interview phase.

Required profiles

We welcome applicants from Earth Sciences (petrology/volcanology/geochemistry/mineral resources), Computer/Data Science (ML/AI), or genuinely interdisciplinary backgrounds.

Typical desirable skills (vary by DC)

• Igneous petrology, mineralogy, geochemistry, volcanology, thermodynamics, modelling; and/or
• ML/AI, data science, statistics, image analysis, scientific programming (Python/R/Julia), HPC;
• Strong motivation for interdisciplinary research and international collaboration;

How to apply

The informations and instructions to apply are provided here: