19 bayesian-object-tracking PhD positions at Technical University of Munich in Germany
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Bayesian machine learning to improve risk management for bridge portfolios. We offer a funded PhD position in an excellent research environment. The project Our infrastructure is aging, and decisions about
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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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. Postdoctoral placement at a top institution/with an active research group/professor (for those interested in the academic track). Placement in a leading research NGO/think-tank/multinational company (for those
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strong interest in research. A proven track record of scientific work, such as prior publications, is beneficial. We particularly value a solid theoretical foundation in 𝗺𝗮𝗰𝗵𝗶𝗻𝗲 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴
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-established and highly visible track record of the laboratory in the analysis of plant growth processes regulated by AGC1 kinases. The Chair of Plant Systems Biology has strong expertise in all relevant
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, adversarial attacks, and Bayesian neural networks. Excellent analytical, technical, and problem-solving skills Excellent programming skills in Python and PyTorch including fundamental software engineering
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measurements in a team of experts on and in the pyramids and creating digital object models with numerical simulations, for example, using Salvus software or similar. Publication of research results and
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to state-of-the-art facilities, including virtual and augmented reality equipment, eye-tracking and facial expression analysis tools, and ECG and electrodermal activity measurement Participation in
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analytical skills for model formulation and optimization Demonstrated research potential, ideally with a track record of publications in relevant venues (journals such as IEEE T-ITS, INFORMS Transportation
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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