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. You will employ the trypanosome model established in our group to study its swimming behavior in soft tissue-like surroundings. This project is a part of the DFG-SPP 2332 priority program “Physics
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: completed scientific higher education degree (PhD) in the field(s) of Earth system science, physics, climate physics, geosciences, mathematics, computer science or a comparable field demonstrated experience
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of bespoke probabilistic models and/or evolutionary simulations, robust knowledge of and an affinity towards mathematical, computational or probabilistic modeling are important. Further skills in modeling and
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this, these simulations need to be massively parallelized. The objective of this thesis is to implement and evaluate different contingency parallelization approaches using our group's computational infrastructure
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. We would therefore strongly encourage qualified women to apply for the position. Your tasks develop surrogate models to approximate high-fidelity phase field simulations, incorporating physics-informed
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: completed scientific higher education degree (PhD) in the field(s) of Earth system science, physics, climate physics, geosciences, mathematics, computer science or a comparable field demonstrated experience
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analysis and visualization Numerical modeling and programming for model optimization & machine-learning What you bring to the table You are studying Simulation Sciences, Mechanical Engineering, Computational
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spiking neural networks Your Profile: Current master studies in physics, computer science, mathematics, electrical/electronic engineering or a related science or engineering field Strong background in
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of electronic devices and circuits as soon as possible. What you will do Development and adaptation of physical models and software tools for the simulation of the manufacturing processes of electronic devices
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. The research program may also involve a numerical simulation component. Your tasks #analyzing measurements of ocean turbulence using autonomous glider vehicles #use and develop machine learning methods