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Field
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and as part of a team Understanding of dynamical systems, time series models, machine learning, Bayesian statistics, experience in handling environmental and climate data is a merit We offer: This
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Helmholtz-Zentrum Dresden-Rossendorf - HZDR - Helmholtz Association | Gorlitz, Sachsen | Germany | 26 days ago
field # Knowledge of dynamical systems and network theory # Previous knowledge of pattern formation theory and of Bayesian inference methods is a plus # Previous programming experience in Python is
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resources. Scientific Objectives: Develop a system using foundation models to detect, classify, and track both stationary and moving objects from multimodal sensor data. Integrate self-learning mechanisms
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), mathematical evolutionary modeling (game theory, dynamical systems, agent-based simulations or other), bespoke probabilistic modeling / (Bayesian) data analysis (e.g., in the Rational Speech Act framework
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of the Horizon Europe research framework program. The consortium maintains the Program Management Unit (PMU), an organizational unit responsible for program planning and program tracking. We are looking for a
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influenced corrosion (MIC) in marine environments. It uses AI-supported models, Bayesian data fusion, and real-time sensor data integration. Your responsibilities include: Development of a digital twin (DT
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risk development. These projections will enable the identification of sustainable adaptation strategies and support policy development. Objectives Develop an Agent Based Model (ABM) that describes flood
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Robotics, Mechanical Engineering, Electrical Engineering, or a closely related field Proven research experience and publication track record in robotic manipulation, deformable object handling, or related
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the teaching of ancient studies. Candidates must have a proven track record in acquiring external research funding. Specific Requirements The appointment will be made in accordance with the general provisions
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analysis Experience in one or more of the following areas is highly desirable: Hybrid modelling, neural differential equations, or physics-informed neural networks Equation discovery Bayesian inference