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in reservoir engineering, geosciences, geotechnical engineering, or related fields. Experience in numerical modeling of flow in porous media and programming skills is highly desirable. Supervisors: Ana
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and numerical models as well as constitutive model calibration and validation based on physical experimental data. Required Qualifications: A successful applicant must have a PhD in Engineering
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phases [1]. The main objective is to study these effects, never observed before in an experimental setting, through numerical modelling and laboratory experiments. This PhD thesis is part of a
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IT4Innovations National Supercomputing Center, VSB - Technical University of Ostrava | Czech | 17 days ago
) at the IT4Innovations, at the Material Design group of Dr. Dominik Legut within the Modeling of Nanotechnology Lab, is open to applicants for studying MATERIAL PROPERTIES OF MAGNETIC MATERIALS by means of the spin
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(HAC). This role focuses on applying advanced computational and analytical methods—including artificial intelligence, machine learning, deep learning, time-series modeling, and large language models
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during these experiments will be used to calibrate a numerical model of PFAS fate in soils. The predictions from this model will then be compared with PFAS concentration measurements in leachate collected
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(if remanent excess heat signatures can be identified near currently inactive hotspots) and small hard-to-resolve anomalies might also be present. In this context, new numerical needs are emerging
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within various scientific and engineering disciplines, including ground-based, airborne, and satellite measurements, numerical modelling, and data analysis. The division's goals are to enhance
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rare-earth ions, with improved quality for optical, magneto-optical and laser applications. Different growth methods will be used such as Czochralski, Bridgman and Flux methods. Numerical modelling will
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%). You will work at the intersection of numerical analysis, uncertainty quantification, and scientific machine learning. The research will primarily focus on probabilistic methods for data-driven model