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of multi-physics and mixed-dimensional PDEs. The goal of the research is the development and analysis of computational methods combined with model order reduction methods, including data-driven ones
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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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in include: 1. How do we engineer cells to turn genes on or off? 2. How do we explore numerous combinations of genetic perturbations in a scalable way? 3. How do we engineer immune recognition
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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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physically based constitutive models for predicting the mechanicalresponse of composites. Development of numerical techniques for multi-scale and multi-physics analysis. Simulation of laboratory tests
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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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National University of Science and Technology POLITEHNICA Bucharest, Pitesti Branch | Romania | about 2 months 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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approached by tools in both computational chemistry and applied mathematics. Specifically, the project involves the development of mathematical modeling strategies at both numerical and analytical level to
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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