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the implications for forest ecosystem service supply. The main methodological approach applied will be forest landscape simulation modelling using the model iLand. The work is embedded in the BETA-FOR project (https
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using the model iLand. The work is embedded in the BETA-FOR project (https://www.uni-wuerzburg.de/for5375/) and will collaborate closely with the Forest Economics and Sustainable Land-use Planning group
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learning and computer simulations. The focus of the PhD project will lie on developing machine learning models for clustering, classification, regression and reinforcement tasks to work with, enhance
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Helmholtz-Zentrum für Infektionsforschung GmbH | Braunschweig, Niedersachsen | Germany | 13 days ago
Coordinate contribution to modelling projects and platforms supporting reporting, communication andmeeting organization Where to apply Website https://jobs.helmholtz-hzi.de/job-invite/654/ Requirements
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7 Apr 2026 Job Information Organisation/Company Technische Universität Dortmund Department Department of Physics Research Field Physics Computer science Geosciences Mathematics Chemistry Researcher
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7 Apr 2026 Job Information Organisation/Company Technische Universität Dortmund Department Department of Physics Research Field Physics Computer science Geosciences Mathematics Engineering
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Approach ( https://vhrz669.hrz.uni-marburg.de/ssf/ ). Understanding the mechanisms controlling subsurface flow (SSF) and the conditions under which it occurs remains a major challenge in hydrology and
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, United Kingdom Supervisor: Dr. Ivana Savic Focus: computational materials modelling Background: physics, computational materials science. or a closely related discipline Apply: https://mgician.eu/research/doctoral
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in cancers of unknown primary (CUP). Your Role You will join Subproject 3 (Model Alignment and Optimization), led by PD Dr. Keno Bressem (https://scholar.google.com/citations?user=wIEgwbkAAAAJ&hl=en
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Helmholtz-Zentrum Dresden-Rossendorf - HZDR - Helmholtz Association | Gorlitz, Sachsen | Germany | 25 days ago
profile # Ph.D. in the field of Physics, Chemistry, Computer Sciences or Applied Mathematics or related field # Background in mathematical surrogate modeling and machine learning # Experience or familiarity