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Bayesian optimization and other active learning techniques to guide experimental efforts by identifying optimal chemical compositions and processing conditions of membranes that maximize both selectivity and
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modeling with experimental validation and has two major objectives: Development of a physics-informed neural network (PINN) framework You will design and implement a simulation framework to model
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alloys. The project is a 3-years funded DFG position. Central to this project is the usage of coherent synchrotron scattering to track transport and structural dynamics in metallic glass. The objective is
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communication systems for AI-driven applications. The objective is to investigate, design, and experimentally validate information-theoretically secure coding schemes tailored to the demanding requirements
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qualified women. About the position The position contains both teaching duties and participation in research projects. The research project topics focus on improving object recognition through computer vision
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Objectives Development of sensitivity framework for coupled sensitivity analysis. Extend the developed framework to support FSI problems, and identify suitable sensitivity computation methods. Identify
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the pyramids and creating digital object models with numerical simulations, for example, using Salvus software or similar. - Publication of research results and presentation of results at scientific conferences
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Bayesian neural networks. Excellent analytical, technical, and problem-solving skills Excellent programming skills in Python and PyTorch including fundamental software engineering principles and machine
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, accounting for cars, buses, trams, bicycles, and trucks as a function of transport supply and travel demand. The objective of the project is first to develop assessment indicators based on the MFD
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plasticity of regulatory T (Treg) cells, in the context of autoimmunity. The primary objectives include: • to compare central Treg cell identity and function programs between mice and men in normal physiology