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demonstrated through previous engagement. Demonstrated knowledge of statistics and causal inference methods / econometrics. What are we looking for in you? Good collaboration skills as well as ability to work
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Are you passionate about railways, acoustics, and data-driven infrastructure solutions? We are looking for a PhD student to join the project “A robust tool for rail fault and roughness estimation
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electron microscopy image simulations Development of a machine learning model capable of inferring 3D atomic structure from two-dimensional TEM projection images Application of the new approach
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to co-design algorithms and circuits to develop efficient neuromorphic hardware, tailored to target tasks. In detail, you will: develop circuit-plausible training/inference algorithms and analyze in
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particular focus on digital methods and tools. The C²DH's ambition is to venture off the beaten track and find new ways of doing, teaching and presenting contemporary history of Luxembourg and the history
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with causal inference techniques such as causal graphical models, instrumental variable analysis, and counterfactual reasoning to better handle high-dimensional, multi-environment datasets typical in
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the driver to actively participate and accept the interventions. A key component in the project will be to practically test feasibility and assess the performance of algorithms and hypotheses in test track
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knowledge of their validity through practical measurements in real conditions. The academic supervision team have a track record of more than 20 years each on multistatic radar research and some 500
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experience: A strong track record of previous research in multilingual NLP Track record of authoring high quality academic publications Excellent programming skills Excellent oral and written communication
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-material capability with a suitable closure model; (2) improved strategy for interface tracking/capturing; (3) very high-speed scenarios with use of nonlinear Riemann-solvers. If time allows exploratory 3D