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Field
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– Coordinating with and assisting the PI in the design of experiment and grant proposal methodology as well as methods for analyzing the results of experiments. 2. Research Publications – Preparation of text
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– Coordinating with and assisting the PI in the design of experiment and grant proposal methodology as well as methods for analyzing the results of experiments. 2. Research Publications – Preparation of text
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Strong background in biomechanics, biomedical engineering, computational mechanics, or equivalent fields. Strong foundation in numerical methods, especially the finite element method. Proficient in python
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and vibrant research team. acquiring new skills in the field of analytical methods and techniques Development prospects: Scientific development – The University offers a wide range of scientific
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establish a research profile. Develop and execute innovative research projects. Develop, train, and evaluate modern machine-learning models on GPU/HPC infrastructure. Integrate AI methods with scientific
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to complex ecological networks design and implement novel coordinate-independent numerical algorithms utilising automatic differentiation and pseudo-arclength continuation methods to identify contact points
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methods for the modelling and causal analysis of mass transit systems. The research will focus in particular on the development and application of data science methods to address key challenges in capacity
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computational methods to investigate quantum, optical, thermal and spin-dependent transport in complex materials. A central expertise of the group is the LSQUANT methodology, a suite of linear-scaling, real-space
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vision/machine learning. Strong foundation in at least one of: numerical linear algebra, Fourier/spectral methods, scientific computing, and/or high-performance computing. Proven ability to publish in peer
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structure methods, quantum Monte Carlo, tensor networks, or quantum embedding methods, etc. - ML-augmented numerical method development. - High-performance computing (HPC