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academic record, with expertise in machine learning, statistical analysis, and numerical methods; ● Proficiency in Python (preferred) or related programming languages such as R, MATLAB, or C++; ● Experience
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satellite observations, climate reanalysis data, and numerical models to better understand how sea ice is transported out of the Arctic and how heat is exchanged between the ocean, ice, and atmosphere. Your
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have: experience or strong interest in geomechanical and/or hydrological modelling using continnum-based numerical methods (e.g., finite element method, finite difference method); experience or strong
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related field; experience with numerical modelling, preferably hydrological modelling; affinity with delta systems, adaptation and policy analysis; motivation to work in interdisciplinary scientific
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an overview of your experience with molecular dynamics, density-functional-theory, machine learning, coding skills, theoretical solid mechanics, numerical analysis. The official transcripts of your BSc and MSc
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-theory, machine learning, coding skills, theoretical solid mechanics, numerical analysis. The official transcripts of your BSc and MSc grades List of at least two references with full contact information
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(ELF). You will therefore collaborate intensively with international partners for support, and measurement analysis will be an integral part of your job. Your qualities You are an enthusiastic and
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adaptation by strengthening energy system resilience against growing climate-related risks. Modeling energy systems presents numerous challenges, which become increasingly intricate when applied at urban
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between these important climate subsystems, using models of different complexity and a combination of mathematical, numerical and data analysis methods. You can find the other vacancy at PhD Position in
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-related risks. Modeling energy systems presents numerous challenges, which become increasingly intricate when applied at urban or local scales due to spatial, temporal, and sectoral heterogeneities