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SFI FAST: PhD position in Microstructure/texture evolution during extrusion of scrap-based Aluminium
development of the FAST Virtual Lab, a digital framework combining experimental data, physics-based models, and data-driven methods to support design, manufacturing, and decision-making across aluminium value
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to metastasize and colonize distant tissues using mouse models, in close collaboration with the Bentires Lab at the University of Basel. This PhD project is fundamentally driven by hands-on experimental in vivo
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future. They represent individuals driven by curiosity and a relentless pursuit of excellence. With us, they find the space to try things out and unfold their potential. Are you inspired by their passion
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of the future, including: the use of faster processors being pushed by CubeSat technology; new requirements for cybers ecurity driven by actual threats; ever-higher data streams and volumes downlinked from
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-fidelity finite-element model structural analyses. The motivation is driven by the desire of the Portuguese leading UAS manufacturer to greatly automate the meshing process, within its in-house UAV design
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impactful, application-driven research. This position supports a strategic research project on full electric harbour craft microgrid modelling, onboard energy management systems (EMS), and hardware-in
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Instituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial | Portugal | 13 days ago
wording published by Decree-Law No. 123/2019 of August 28; Regulation of Research Grants of the Foundation for Science and Technology, I.P. in force (Regulation No. 950 / 2019 of 16 December https
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About the Opportunity Job Summary The Snowflake Developer is responsible for designing, developing, optimizing, and operationalizing Northeastern University’s Snowflake data platform. This role
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publication record. Outstanding data analytics, mathematical, and computer modelling skills. Excellent interpersonal communication and oral presentation skills in English Self-driven and strong team spirit Open
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direction investigates the role of attention mechanisms in event-driven and spike-based models. Transformer architectures will serve as a reference framework to analyze the benefits and limitations