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ALMA MATER STUDIORUM - UNIVERSITA' DI BOLOGNA - - DIPARTIMENTO DI SCIENZE MEDICHE VETERINARIE | Italy | 12 days ago
1.2, the work will establish a murine AD-diabetes comorbidity model and generate metabolic and cognitive datasets. Objectives include: establishing diabetic models (STZ and HF diet), serial glucose
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Keen to push the frontiers of multiphase reactor modeling and accelerate the scale-up of emerging net-zero technologies? Join us at the Department of Chemistry and Chemical Engineering! About us Our
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▪ Knowledge of jamming, interference modeling, or secure communication techniques ▪ Familiarity with ray tracing tools or channel modeling software Our offer ▪ Access to state‑of‑the‑art lab facilities
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The role The University of Bristol wishes to recruit a highly skilled & qualified graduate / post-graduate to lead and deliver a Knowledge Transfer Partnership (KTP) with iCOMAT Limited, a
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information please visit: https://www.brunel.ac.uk/about/our-history/home We are seeking to appoint a Research Assistant who will work on the ELOQUENCE European project which aims to study Large Language models
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Overview This 12-month post is associated with an EPSRC grant led by Imperial College. The grant is titled “The effect of hip muscles on knee pain” and involves development of a multi-body model of
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experience with DFT codes will be very highly valued. • Knowledge of chemical reactions and how to model them through computer simulations is highly valued. • Knowledge of classical molecular dynamics
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Helmholtz-Zentrum Dresden-Rossendorf - HZDR - Helmholtz Association | Gorlitz, Sachsen | Germany | 3 days ago
, Computational Modeling and Simulation or related field # Experiences with advanced numerical analysis and mathematical modeling of partial differential equations # Knowledge of quantum algorithms or willingness
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degree is foreign, recognition is requested) b) The candidate must have experience/knowledge in software development, data engineering, data science and implementation of ontology-based models, in
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learning to automatically build models from experimental data while directly embedding physical and mathematical knowledge within the learning architecture. This hybrid approach produces more reliable models