170 parallel-computing-numerical-methods positions at Technical University of Munich in Germany
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methods for the design, verification, and test of circuits and systems for conventional as well as alternative and post-CMOS computing technologies. Besides that, we have successfully applied the methods
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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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22.10.2020, Wissenschaftliches Personal PhD and PostDoc Positions in Visual Computing & Artificial Intelligence: we are looking for highly-motivated PhD students and PostDocs at the intersection
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us We are TUM’s unique Pathology AI lab developing new machine learning (ML) methods for automatically analyzing digital pathology data and related medical data. Such methods include the automatic
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numerical studies. Develop NanoLPC application in additive manufacturing by developing a multiscale simulation tool for keyhole dynamics and pore formation prediction suitable for PBF applications. Expected
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of the structure, dynamics and kinetics of these systems, we mainly use scattering methods and do experiments at large facilities. Time-resolved scattering experiments at large-scale facilities allow us to
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to the road? Then this position is just right for you! About us In the Autonomous Vehicle Lab, we develop the vehicle of the future with intelligent algorithms and methods. We are involved in numerous projects
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these materials into high-performance fibers and functional materials. By manipulating molecular interactions through chemical and physical methods, we tailor the structural and mechanical properties of bio-based
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investigation of the aerodynamic performance of advanced future compressor stages, support-ed by numerical modelling and simulations of performance-enhancing design features. In this research project you will be
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the possibility of an extension. TASKS: Mathematical modeling and development of inverse methods (e.g. Bayesian inversion, optimization based methods, sparsity promoting methods based on L1-norm minimization and