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description This project addresses the effective design of a military supply logistics network, composed of transportation and communication links such as roads and rail, aerial drone routes, and nodes, such as
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Applications are invited for a Doctoral Candidate in SHIELD (Strategies for Healing Implant-Associated Infections and Enhancing Longevity in Devices) Doctoral Network. The candidate would be working
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array antenna systems for imaging MIMO radar in autonomous driving applications. This work will advance the design and characterization of intelligent devices and environments for wireless communications
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in computer vision and language technology Direct impact on accessibility and inclusion for the Icelandic deaf community Participation in international conferences and research networks Mentorship from
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PhD Position in Theoretical Algorithms or Graph and Network Visualization - Promotionsstelle (m/w/d)
of Munich (TUM), Campus Heilbronn. We are looking for exceptional candidates who are interested in pursuing a PhD in either theoretical computer science or graph and network visualization. We seek PhD
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/RDF knowledge graphs, SPARQL endpoints, and open benchmarking suites to guarantee FAIR, reusable research data Mine and link structure-property relationships from DFT, MD, phase-field, TEM/SEM, and
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of relevant work experience as a post-doc and/or in an R&D position, excluding time associated with family planning, military service, illness or other life-changing events. Brookhaven Laboratory is committed
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infrastructures, urbanization, and climate change are intensifying the vulnerability of critical infrastructures (CI), high-tech industries (HTI), and communities to cascading hazards, particularly NaTech (Natural
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Job Description Apply now Req Id: 35651 Job Title: Post Doc Research Associate City: West Lafayette Job Description: Job Summary The Center for Food Demand Analysis and Sustainability (CFDAS
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-mechanical phase-field model incorporating hydrogen diffusion, mechanical degradation, and fracture evolution. - Employ physics-informed neural networks (PINNs) to infer hidden fields and accelerate