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in modeling and analysis of cyber-physical systems At the Technical Faculty of IT and Design, Department of Computer Science, a full-time postdoc position in modeling and analysis of cyber-physical
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of the project “PROSPER: Predictive models for sustainable protein recovery”, funded by FEDER and by National Funds through FCT (Operation No. 15391 — COMPETE2030-FEDER-00907300), under the following conditions
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allowing to recognize, summarize, translate, predict, and generate contents using medium to large or very large datasets: in particular new approaches based on Large Language Models (LLM) will require
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and integration of multimodal neuroimaging, behavioral and clinical data, and building large-scale deep learning models for multimodal neuroimaging datasets to construct predictive network models in
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, Python, and SAS or Stata) • Demonstrated expertise in causal inference and high-dimensional risk adjustment/predictive modeling, experience with Medicare claims data • Clear scientific writing and
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project activities, as predicted in the application: Activity 2. TRL 5 cultivation and AI model development Activity 3. Biomass analysis and process techno-economic and sustainability assessment
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stars and potentially very massive stars (> 100 Msun). Observations will be compared with predictions from synthetic population synthesis models accounting for single and binary evolution, plus various
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on the project can be found here: https://hecustom.eu/ This post will contribute to the creation and validation of a digital twin (with biological bone models) to assess and interrogate the issue of
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to advance predictive modeling frameworks for soft, rate-dependent materials under complex thermo-mechanical loading conditions. The research will involve close integration of analytical constitutive modeling
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the integration of high data-density reaction/bioanalysis techniques, organic synthesis, laboratory automation & robotics and machine learning modelling. This exciting project involves the application of innovative