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develops an adaptive AI-guided XR platform for capturing and transferring expert manufacturing knowledge. Your focus will be on developing AI methods for analyzing and modeling human workflows based on data
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Climate change and human disturbances are placing increasing pressure on Europe's forests, demanding advanced methods for early detection and monitoring. We are seeking a research assistant to
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is to combine multi-gene control technology and computer algorithms to develop a foundational discovery platform for future cell programming applications. This position involves both experimental and
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most rapidly in the boreal region, will require adaptive management strategies. For this purpose, a transformation from traditional rotation forestry towards continuous cover forestry methods is debated
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in redox flow batteries electrodes. Within this project the DC will use pore-scale direct numerical simulations (based on the lattice-Boltzmann method) to enable the precise quantification of mass
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methods in High-Energy physics, in particular quantum field theory and particle physics is required. Familiarity with symbolic computer algebra systems such as Mathematica is required You will need strong
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handful of them have excited states with the opposite parity as the ground state. Hence, in most cases, traditional spectroscopic methods cannot be applied to negative ions, leaving many of them poorly
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capabilities of nonlinear quantum systems, employing tools from quantum information theory and quantum metrology. The work will involve learning and applying mathematical methods to solve open quantum dynamics
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no more than three years prior to the application deadline*. A working knowledge of advanced methods in High-Energy physics, in particular quantum field theory and particle physics is required. Familiarity with
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: Help develop a non-invasive computer vision method to track and analyze how hens move in 3D space. You will gain hands-on experience in behavioural studies, animal welfare science, and innovative data