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Field
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Experience working with computation clusters and managing large datasets Proven ability to develop, maintain, and optimize scientific software Experience with scientific data visualization and related tools
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projects from low level hardware interfaces to high level optimized applications mainly in Python and under Linux Increasing the functionality and reliability of the beamlines in order to realize
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, including finite-element simulation and topological optimization of light guidance in HCFs, and numerical simulation of thermo- and fluid dynamics under fiber-drawing processes. Apart from the main tasks
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control for medical robotics in the context of cardiovascular technologies. The goal is to innovate control systems for optimized interaction of soft cardiovascular pumps and wearable biofeedback systems
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. Our research integrates expertise from machine learning, optimization, control theory, and network science, spanning diverse application domains such as energy systems, biomedical systems, material
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is expected that they will actively and creatively develop and optimize the detailed methods to pursue the overall project goals and, after a training period, independently analyze genomic data using
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learning, mathematical statistics, optimization, and robotics. Experience from programming in C/C++ or Python is also meritorious. Willingness to work in an inter-cultural, international, and diverse group
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systems. This PhD project, part of a national initiative, aims to use AI to design and optimize thermal interface materials (TIMs). It combines machine learning, materials informatics, and experiments
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computing, with a focus on performance analysis, development, and optimization of scientific simulation codes. The work involves applications in plasma physics, computational fluid dynamics, and molecular
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mathematics, such as extreme value theory, inference for stochastic processes, optimization theory, and/or Monte Carlo simulations. Experience in obtaining research grants in national and/or international