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workflows for descriptor based microstructure reconstruction to identify material parameters for crystal plasticity simulations from experimental data through inverse analysis to establish structure–property
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Simulations (LES). The analysis will be performed together with teams at the Helmholtz-Zentrum Hereon that focus on ocean turbulence and machine learning as a part of the TRR181 Collaborative Research Centre
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and/or computer-based simulations, and a formal dissertation-style report. We are seeking to increase the diversity within our community and would particularly encourage applicants from underrepresented
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physical laws, or an implicit form of extra data examples collected from physical simulations or their ML surrogates. In medical domains, patient data is typically distributed across multiple hospitals
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on developing machine learning algorithms to support the use of complex urban simulators in decision-making under uncertainty. This PhD project shifts the focus from optimality to relevance in urban land-use and
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the subject of the email: a downloaded copy of your application form. a short project proposal outlining how you would approach the project (up to 2,000 words, excluding references). a full academic CV (maximum
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-mature.org and send it together with a cover letter, a curriculum vitae, a copy of all university degrees and other certificates in a single pdf-file to gerd.bacher@uni-due.de . Please mention in your
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University of Massachusetts Medical School | Worcester, Massachusetts | United States | about 2 months ago
to the preparation of grant proposals and scientific publications. Interested applicants should write a cover letter explaining their interest in the research project, copy of their CV and contact information of three
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(28nm or below) Hardware-aware modeling of state-space inference pipelines Simulation and synthesis of the architecture using EDA tools Benchmarking against transformer-based hardware accelerators Work
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Hardware-software co-simulation and benchmarking This PhD project is part of SDU microelectronic unit’s effort in neuromorphic chip design and collaborates with international partners working on spiking AI