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Brandenburgische Technische Universität Cottbus | Cottbus, Brandenburg | Germany | about 2 months ago
research focus areas include stochastic simulation and approximation methods (for example, Markov Chain Monte Carlo, interacting particle systems, PINNs) or stochastic optimization (e. g., stochastic
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using Monte Carlo and molecular dynamics methods, along with analysis of the obtained results familiarity with scientific computing tools and environments, such as Wolfram Mathematica readiness to conduct
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datasets. Develop quantitatively predictive models of biological systems. Integrate multi-omics data into quantitative computational models. Apply Monte Carlo sampling approaches to quantify uncertainty
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networks (GNNs) is a plus Experience with modeling and simulation techniques, such as: Network, agent-based, or discrete-event simulation Monte Carlo or stochastic simulation methods Simulation-in-the-loop
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at the water/steel interface during water radiolysis using two complementary computational approaches. The first approach involves Monte Carlo simulations using the GEANT4-DNA and GATE codes. GEANT4 will be
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characterization of germanium detectors, develop advanced Monte Carlo models and AI-driven analysis tools to optimize detector response, and to promote precision medical imaging and low energy dark matter searches
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theory together with mean-field modeling and Monte-Carlo simulations for reaction kinetics. By linking quantum mechanical calculations with kinetic modeling, it is possible to bridge both length and time
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(medallion architecture, incremental loads, schema management) is expected. Data Observability and Pipeline Monitoring: Experience with data observability platforms (such as Monte Carlo, Acceldata, Anomalo
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dynamics simulation techniques, quantum mechanical calculations and the site identification of ligand competitive saturation (SILCS) methodology are required. Knowledge in Monte-Carlo methods, including in
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experience with graph‑based ML or GNNs is a plus Experience or coursework involving modeling and simulation techniques, such as: Network, agent‑based, or discrete‑event simulation Monte Carlo or stochastic