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for a closed-loop decision support system that can be verified in rehabilitation in many health conditions. This position is open for a postdoctoral researcher in the field of transparent machine learning
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scientist with strong skills in machine learning and hydro-meteorology. The main tasks of the successful candidate will be to translate the most recent developments in the fields of machine learning and Earth
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100%, fixed-term We are seeking a postdoc in biogeochemical modelling to work on the SNF-funded N2O-SSA project: Combining measurements, modelling and machine learning to improve N2O accounting
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novel methods at the intersection of advanced control, optimization, manufacturing science and machine learning, to create the next generation of sustainable automation solutions for modern manufacturing
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scenarios and impacts, or machine learning/data science applied to environmental problems. Project background Successful participants could use coupled global (CMIP) simulations, design and set up new model
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combine computer-aided molecular and process design to optimize molecules and processes simultaneously. To holistically evaluate the environmental impacts of chemicals and energy systems, we develop
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machine learning to develop an automated design process of mechanical walking aids, analyse gait patterns and make biomechanical simulations embedded in the generative mechanism design process. In
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simulations, laboratory experiments, and field analyses. Our aim is to gain fundamental insights and develop sustainable technologies to address societal needs. Fluid injection or production induces changes in
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variability described in the data by means of uncertainty aware calibration and Bayesian estimation Include how experts currently operate and acquire feelings for the machine they are driving, also based on a
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construction or related fields Knowledge and experience in data analysis and database management, as well as emerging technologies applied to the construction sector, such as BIM, scanning, machine learning