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Helmholtz-Zentrum Dresden-Rossendorf - HZDR - Helmholtz Association | Gorlitz, Sachsen | Germany | 11 days ago
(Master/Diploma) in the field of Chemistry, Physics, Computer Science or related field # Knowledge of Density Functional Theory (DFT) # Familiarity with artificial intelligence methods # Good knowledge
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linear algebra, numerical methods for PDEs and dynamical systems, stochastic methods in statistical mechanics, hydrodynamic limits, interacting many-body systems, quantum macroscopic evolution equations
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, Process Engineering, Computational Science, or a related discipline Strong foundation in fluid mechanics, gas–liquid two-phase flows, numerical methods (FVM, FEM), and two-phase flow instrumentation
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, Physics (computational or solid-state focus), or a related field Strong interest in additive manufacturing, topology optimization, computational design, and process modelling Background in numerical methods
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at the crossroads of AI, machine learning, bioinformatics and genomics, and in developing new methods rather than just applying existing ones, we'd like to hear from you. Website for additional job details https
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-Documented ability to teach a range of core and advanced courses in mathematics (e.g., calculus sequence, linear algebra, differential equations, probability and statistics, numerical methods, optimization
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, in particular in quantum phase-space methods. • You have a proven experience in numerical methods and programming, particularly the simulation of PDEs. • Excellent written and spoken English. What we
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of the hotspot quantification with the nanoagent size and the optical fluence for instance, and (3) numerically modeling the hotspot measurement with the photoacoustic method to link the mesoscopic measurement
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methods, which could include but are not limited to: Kriging surrogate, Polynomial Chaos Expansion (PCE), and Physics-Informed Neural Networks (PINNs) Contribute to the strategic direction of research
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position is the development of novel machine learning methods for modeling molecular properties, in particular regression models for bi-molecular properties. The research is embedded in the thematic context