Ph3Position /h3 p15 PhD positions as part of the MSCA-DN REALISE "Bridging Igneous Petrology and Machine Learning for Science and Society" /p h3Employer /h3 pMSCA-DN REALISE consortium /p pThe REALISE consortium (University of Perugia, Sorbonne and IPGP Paris, KU Leuven, ELTE Budapest, Leibniz University Hannover, University of Ljubljana, CNRS Orleans) brings together leading Earth scientists, specialising in bigneous petrology, volcanology, and ore‑deposit genesis, with experts in data science and machine learning /b. Our consortium includes 8 beneficiaries and 10 associated partners. /p pThe REALISE consortium bcombines industrial know‑how, academic excellence, and an entrepreneurial mindset /b to train a new cohort of creative, responsible innovators ready to tackle the challenges of bnatural‑risk assessment and the economy of critical raw materials /b. REALISE has a strong binternational profile /b across seven EU countries (Italy, Hungary, France, Belgium, Germany, Slovenia, and the Netherlands), with commercial partners that extend the consortium in North America and the United Kingdom. /p pHomepage: /p h3Location /h3 pPerugia, Italy /p h3Sector /h3 pAcademic /p h3Relevant division /h3 pGeochemistry, Mineralogy, Petrology Volcanology (GMPV) /p h3Type /h3 pFull time /p h3Level /h3 pStudent / Graduate / Internship /p h3Salary /h3 pThe 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. /p h3Required education /h3 pMaster /p h3Application deadline /h3 p31 March 2026 /p h3Posted /h3 p22 February 2026 /p h3Job Description /h3 pMSCA-DN - REALISE "Bridging Igneous Petrology and Machine Learning for Science and Society" will train b15 Doctoral Candidates /b 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: /p ul liVolcanic hazard assessment and risk mitigation /li liThe understanding and exploration of critical raw materials in magmatic systems. /li /ul pDoctoral projects address magma lifecycle processes, multimodal data fusion, physics–ML hybrid modelling (from CFD to atomistic simulations), and AI‑assisted hypothesis formulation. /p h3MSCA Doctoral Candidate eligibility criteria /h3 pApplicants must comply with the Marie Skłodowska‑Curie Doctoral Network eligibility rules, including: /p ul liApplicants must not hold a PhD degree at the time of recruitment. /li liAt 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. /li /ul h3Other Requirements /h3 ul liApplicants must meet the doctoral admission requirements of the recruiting host institution. /li liExcellent motivation for interdisciplinary research combining Earth Sciences and ML/AI is expected. /li liStrong English communication skills are required. /li /ul h3Overview Of The Doctoral Candidate Positions /h3 pEach position is a bfully funded 36‑month PhD fellowship /b within the MSCA framework and includes international secondments, advanced training, and competitive employment conditions according to MSCA rules. /p h3First Call Application deadline /h3 p31 March 2026 (Positions will remain open until filled) /p h3Remuneration /h3 pThe remuneration is based on the MSCA-DN researcher allowances (Living allowance: b4,010 € /b (adjusted by country coefficient); mobility allowance: b710 € /b; family allowance: b660 € (if eligible) /b). 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. /p h3Available Positions /h3 h3DC1 – Deciphering the genesis of critical ore deposits in the Bushveld Layered Intrusions /h3 ul liHost/PhD: University of Perugia (Italy) | Supervisor: Prof. Maurizio Petrelli /li liSecondments: BGR (Germany); UvA (Netherlands) /li /ul h3DC2 – Pre‑eruptive dynamics at Campi Flegrei: multimodal petrology + physics‑informed AI /h3 ul liHost/PhD: University of Perugia (Italy) | Supervisor: Prof. Maurizio Petrelli /li liSecondments: KUL (Belgium); UL (Slovenia) /li /ul h3DC3 – ML‑assisted atomistic simulations: partitioning of critical raw elements in magmas /h3 ul liHost/PhD: University of Perugia (Italy) | Supervisor: Prof. Maurizio Petrelli /li liSecondments: IPGP (France); LUH (Germany) /li /ul h3DC4 – Generative AI for super‑resolution artificial 3D tomography of crystal zoning /h3 ul liHost/PhD: Leibniz University of Hannover (Germany) | Supervisor: Prof. Monika Sester /li liSecondments: NU (UK); ELTE (Hungary) /li /ul h3DC5 – Data‑driven investigations of magma plumbing systems at active volcanoes /h3 ul liHost/PhD: Leibniz University of Hannover (Germany) | Supervisor: Prof. François Holtz /li liSecondments: KUL (Belgium); UvA (Netherlands) /li /ul h3DC6 – Multi‑phase ML thermo‑chemo‑barometry of volcano plumbing systems /h3 ul liHost/PhD: KU Leuven (Belgium) | Supervisor: Prof. Olivier Namur /li liSecondments: LUH (Germany); UNIPG (Italy) /li /ul h3DC7 – AI‑assisted mineralogical/textural characterization of LCT pegmatites /h3 ul liHost/PhD: KU Leuven (Belgium) | Supervisor: Prof. Anouk Borst /li liSecondments: BGR (Germany); UORL (France); LUH (Germany) /li /ul h3DC8 – DeepEruptive: AI for eruptive parameter estimations (petrology + CFD + tephra maps) /h3 ul liHost/PhD: Sorbonne University (France) | Supervisor: Prof. Paola Cinnella /li liSecondments: INGV (Italy); Ainoudo (France) /li /ul h3DC9 – Melt inclusions: ML analysis of hyperspectral data for magma reservoir conditions /h3 ul liHost/PhD: Sorbonne University (France) | Supervisor: Dr. Hélène Balcone‑Boissard /li liSecondments: SOLEIL (France); KUL (Belgium) /li /ul h3DC10 – ML‑assisted multimodal workflows for sulfide accumulation processes in ore deposits /h3 ul liHost/PhD: CNRS/University of Orleans (France) | Supervisor: Dr. Giada Iacono‑Marziano /li liSecondments: Teledyne Photon Machines; INFN (Italy) /li /ul h3DC11 – AI‑supported hypothesis formulation on metal‑rich granites (critical metal prospectivity) /h3 ul liHost/PhD: CNRS/University of Orleans (France) | Supervisor: Dr. Fabrice Gaillard /li liSecondments: BRGM (France); UNIPG (Italy) /li /ul h3DC12 – Petrological monitoring eruptive style forecasting in open‑vent volcanoes (Etna) /h3 ul liHost/PhD: Eötvös Loránd University (Hungary) | Supervisor: Dr. Réka Lukács /li liSecondments: UL (Slovenia); Italian Civil Protection Department (Italy) /li /ul h3DC13 – ML for hypothesis formulation + trace‑element thermodynamic insights in silicic systems /h3 ul liHost/PhD: Eötvös Loránd University (Hungary) | Supervisor: Prof. András Lukács /li liSecondments: UNIPG (Italy); KUL (Belgium) /li /ul h3DC14 – DEGAS: ML‑based atomistic simulations of volcanic degassing /h3 ul liHost/PhD: Institut de Physique du Globe de Paris (France) | Supervisor: Prof. Razvan Caracas /li liSecondments: INFN (Italy); UORL (France) /li /ul h3DC15 – Large Language Models for automated data explanations in petrology (incl. multimodal fusion) /h3 ul liHost/PhD: University of Ljubljana (Slovenia) | Supervisor: Prof. Blaž Zupan /li liSecondments: UNIPG (Italy); UvA (Netherlands) /li /ul h3Required Profile (general) /h3 pWe welcome applicants from bEarth Sciences (petrology/volcanology/geochemistry/mineral resources) /b, bComputer/Data Science (ML/AI) /b, or genuinely interdisciplinary backgrounds. /p h3Typical Desirable Skills (vary By DC) /h3 ul liIgneous petrology, mineralogy, geochemistry, volcanology, thermodynamics, modelling; and/or /li liML/AI, data science, statistics, image analysis, scientific programming (Python/R/Julia), HPC; /li liStrong motivation for interdisciplinary research and international collaboration; /li liGood English communication skills. /li /ul h3How to apply /h3 pApply via: instructions and links on (project website). /p /p #J-18808-Ljbffr