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-informed / simulation-aware modeling Efficient algorithms for design-space exploration (e.g., surrogate modeling, Bayesian optimization, differentiable programming) Hybrid approaches combining data-driven
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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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, groundwater flow simulation, and numerical model development Basic knowledge of numerical methods for solving nonlinear systems of partial differential equations (e.g., finite volume method, finite element
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Job Advertisement 2026-03 At the Leibniz Institute of Atmospheric Physics (IAP), a part-time position (75%) in the Department “Modelling of Atmospheric Processes” is available as PhD student
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independently and collaboratively Preferred Qualifications: Experience with multi-phase flow simulation, reactive-transport simulation, groundwater flow simulation, and model development Basic knowledge
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₂, and magnetic Weyl semimetals). Work is computationally driven and focuses on linking quantum calculations to reduced, physics-aware spin models so that materials can be ranked and engineered for device
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on model approach to improve vegetation’s impact on energy fluxes and related permafrost dynamics improve the process-based fire model SPITFIRE to better simulate fire effects on tundra vegetation and
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Your Job: This PhD project focuses on modelling and simulating future gas grids, exploring transformation pathways, and developing cross-sectoral simulation frameworks to support informed decision
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to correct or account for these biases, and build predictive models that simulate biological responses to in silico perturbations such as genetic or pharmacological interventions. The project aims to advance
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(doctoral researcher) to enhance our research group. The candidate will contribute to the numerical modelling, the analysis, and the interpretation of marine biogeochemistry model simulations based on NEMO