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Field
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simulations. Job Description Are you passionate about bridging computational modeling with clinical cardiology to solve real-world healthcare challenges? We're seeking a PhD candidate to develop innovative
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, foreseen in the application: 1-Development of the SUPERB framework 2- Definition of building classes and numerical models for physical vulnerability assessment 4-Definition of baseline data reflecting
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bring certified copies of certificates and diplomas upon request. The application must include: Transcripts and diplomas for Bachelor's and Master's degrees CV Copy of Master's thesis Your research plan
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cases for the position are related to virtual flow metering and well inflow applications. Research tasks include feature identification and selection for robust and trustworthy models. The PhD candidates
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treatment processes through advanced machine learning, validated against physics-based models and experimental data. System Integration: Integrating the DTs into material and energy balance equations
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analyze mathematical models (primarily ODEs, but also PDEs and stochastic models) of viral replication and immune processes. Implement simulations and perform computational experiments using high-level
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datasets using programming languages such as R, SAS, Stata and Python A strong quantitative background in mathematical and simulation modelling, especially Markov-chain, common decision-analytic model
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. As part of this PhD, the candidate will: Conduct an integrative review of established competency models Create assessment tools (which may include use of AI tools) to measure CLMA proficiency
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such as model-based optimal control and nonlinear reset control. The goal is to push beyond commercial standards, achieving unprecedented sensitivity by overcoming mechanical and interferometric noise
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analog circuits for implementing ONNs for computing. Modeling, simulate and benchmark different computing tasks such as sensor data processing. Explore ONN implementation topology and its energy efficiency