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π½πΌππΆππΆπΌπ» The PhD focuses on safe and reliable loco-manipulation for mobile and interactive robots such as humanoids. While learning-based methods enable advanced locomotion and manipulation, deploying them
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methods to tackle challenges in scientific modeling. Affiliations: Technical University of Munich (TUM) & Helmholtz Munich Niki Kilbertus invites applications for a fully funded PhD position. Weβre looking
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travel and in-field research. Ability and eagerness to self-learn statistical or design methods with minimal supervision. Strong written and spoken English. Eagerness to work as part of a team. How to
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of rainfall, drainage capacity, and 3D urban form on flood severity β’ Validate results with hydrodynamic simulations and 3D urban semantic models, benchmark against state-of-the-art methods, and publish in
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processing and machine learning methods, and big data analytics solutions to extract highly accurate large-scale geo-information from big Earth observation data. Our team aims at tackling societal grand
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-Euclidean spaces Scalable layout methods and visual analytics for large and dynamic networks Dimensionality reduction and embeddings for visual exploration Interaction design and user-centered evaluation
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, or a related discipline Interested in climatology/meteorology as well as quantitative methods Prior experience in programming is a plus (e.g., using R or Python) Good communication skills and a high
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: Completed master studies in the field of environmental sciences, forestry, landscape ecology, remote sensing or related fields Interested in remote sensing, quantitative methods and programming Prior
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are pursued by this Center: (1) develop a new set of consistent scientific methods for mobility planning and management, (2) integrate a new set of modular metrics for responsible mobility, (3) embed
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knowledge of quantitative methods, particularly in statistics and econometrics; experience in machine learning is a plus ο Background in business/management/behavioral science ο Experience with programming