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Life and Health Sciences Research Institute (ICVS), from the School of Medicine (EM) of the University of Minho | Portugal | 4 days ago
“Application deadline and submission” of this Call, the candidates must submit in the application process a Declaration of Honor regarding the benefited Research Fellowships, issued as defined in the model
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Modeling, Analysis & Prediction of Particle-laden Real-Gas Supersonic Turbulence. (Ref. 10267290001)
Description Mission: Carry out the modeling, analysis and prediction of real gas supersonic turbulence. Fuctions to be developed: Develop tools to aid analysis. Perform experimental and computational analyses
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signaling models”. The scholarship is full-time for 2 years, with access starting in May 2026 or by agreement. The research will be carried out in the laboratory of Cemal Erdem at the Department of Medical
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derived spheroids Develop predictive model of drug response by comparing 2D to 3D cellular systems Testing and validating the relevance of such models in patient tumour specimens Support and preparation
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biological applications. You will design and implement models ranging from molecular to process scales, develop model-predictive control and optimization strategies, run high-performance numerical experiments
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industrial decarbonisation modelling to support the EU-funded FLARE project. The role will lead the technical development and integration of bottom-up, organisation-level decarbonisation models for energy
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-edge in silico, in vitro, and in vivo technologies to understand, predict and treat thrombosis? This is your chance!! Our goal: Develop multi-level thrombosis risk prediction models by integrating
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data · Demonstrated expertise in causal inference and high-dimensional risk adjustment/predictive modeling · Clear scientific writing and communication, an ability to work both independently and in teams
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to validate, interpret, and document financial data, resolve discrepancies, and clarify business rules. Responsibilities Develop reports and analytical insights for predictive budgeting models that support
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capacity. We are increasingly incorporating machine learning models into our design-build-test iterations to predict protein function. The group’s focus is predominantly enzyme design, but readily touches