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include the design and implementation of finite element multiscale models and machine learning algorithms, analyzing related experimental data, and collaborating with industrial collaborators to validate
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Itai Ashlagi and the Stanford Impact Lab on Equitable Access to Education are seeking a diverse pool of applicants who wish to join a team-based, collaborative community and who value the different
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dynamics, solid mechanics, soft matter or active matter. • To become familiar with simulation algorithms as needed, assist in the development of new ones, test and document any newly developed
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of the following: Ecosystem Modeling, Machine Learning, Microbiome, Microbial Ecology, Soil Science, or Computational Biology. The positions are for several different projects, including the following: (P1
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profile, experience and research proposal. Planning and autonomy: The objective is to study the state of the art of planning algorithms that would support onboard autonomous operations of a rover system on
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MesaPD to solve complex multiphysics problems. The coupling is done across package boundaries. This also requires more sophisticated approaches in load-balancing. Finally, the newly developed algorithms
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computer science with very good results - Interest on topics around the area of distributed systems and data management - Basic knowledge in distributed systems and graph algorithms is desired - Hand-on experience
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to study effects of (TI)-DBS on ex vivo brain slices and in vivo rodent models. Develop advanced closed-loop neurostimulation algorithms. Engage in a cross-disciplinary team, collaborating with experts
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interdisciplinary cooperations with partners and stakeholders from different domains. Candidates should have completed their Doctoral studies in Computer Science, Mathematics, Mechatronics, Electrical Engineering or