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groups. The theoretical methods include stochastic modelling, MD simulation, and Bayesian inference; the position will also include joint collaborative projects with other group members and our
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, hydrodynamic data, infrastructure data, etc.) will be used in combination with already existing tools for maritime traffic simulation (e.g., OpenTNSim, which is being developed at TU Delft), energy consumption
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building, Renovation of built environment, and Circular construction and infrastructure. Over the course of 2 years (24 months), your work will include: Developing a practical tool for social housing
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systems into vehicle platforms. The position includes the design and execution of simulator-based studies, development of predictive comfort models, and validation of thermal comfort solutions using diverse
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information about our research group can be found at: https://autonomousrobots.nl/ Job requirements We are looking for a candidate with: A PhD degree in Robotics, Control, Aerospace Engineering, Mechanical
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, reliability, multi-physics and multiscale simulation. Demonstrated publication record in high-quality international journals and conferences. Experience working in multidisciplinary and international research
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discipline. Strong background in electronic structure methods and molecular simulations. Experience with post-Hartree-Fock methods (ideally CC and EOM-CC) and with quantum embedding strategies is highly
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, society and economy. The Φ-lab brings together early career and senior researchers from a variety of disciplines across EO in pursuit of disruptive/transformative innovation to contribute to the development
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Mathematics, or a closely related quantitative field. You love physics and complex systems and are either familiar with, or very eager to learn about, (road) network traffic flow theory and simulation. You are
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topics such as Hamilton-Jacobi Bellman equations, stochastic optimisation frameworks, game-theoretical analysis of space logistics, high order automated differentiation, neural and Hamiltonian ODEs, data