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implementation of numerical models for the simulation of wave propagation from weather-tsunamis and on the development and implementation of hydrodynamic models (2D/3D) for the simulation of the transport
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mathematical modeling and numerical methods. Detailed information is available on the ACHILLES website. Interviews - Recruitment workshop: April 2026. The top three candidates for each position will be invited
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criteria - Master simulation tools such as SPICE, VHDL(-AMS), Cadence, as well as calculation and modeling tools such as Matlab/Octave or Python. Skills in finite-element type numerical simulation will be a
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. The specific focus of this position will be on the theoretical design of quantum platforms and models to improve upon existing error correcting codes, while leveraging platform-specific physics knowledge, be it
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mathematics, Earth science, or a related discipline Skills in numerical modelling, programming, and handling large datasets Prior experience in machine learning is desirable Interest in nonlinear dynamics and
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of quantum gases; Anderson problem - Numerical simulations of disordered and chaotic quantum dynamics This postdoctoral project focuses on studying a newly observed subdiffusive transport mechanism in
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mineral and metal-bearing raw materials more efficiently and to recycle them in an environmentally friendly way. The Group Geometallurgy and Particle Based Process Modelling is looking for a PhD Student (f
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National University of Science and Technology POLITEHNICA Bucharest, Pitesti Branch | Romania | about 1 month ago
processing algorithms, detection and characterization of current ripple, modeling the behavior of electrochemical systems and implementing adaptive control strategies for voltage and current stabilization
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applicant will be responsible for developing and applying thermal and dynamic models for modelling behaviour of a liquid hydrogen tank, coupled to the behaviour of the contained fluid, as well as aspects
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at the interface of biological physics, agent-based simulations and machine learning to turn quantitative imaging data into a mechanistic, testable model of spindle positioning. In particular, we expect