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data, in situ measurements by using the AI/ML-based result analyses and methods. In this role, you will: Focus on AI/ML methods to improve hydrological observations using the GNSS-Reflectometry Network
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–Tree Duality, to enable large-scale multiloop computations at nonzero temperature and chemical potential. The project aims to overcome long-standing challenges in finite-temperature and finite-density
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computational models (e.g., Convection-Diffusion Equations, Finite Element Methods, Computational Fluid Dynamics) to investigate cerebral blood flow (CBF), neurovascular coupling (NVC), and neurovascular system
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three-year doctoral research project that develops synthetic aperture radar-based sensing methods, models, and experiments targeted for human health monitoring in specific applications. The goal is to
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mechanical engineering, mechatronics, robotics, computational mechanics, control, or a closely related field. We value experience or clear interest in the following areas: Structural dynamics / finite element
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using language identification, such as text corpora for rare languages. Language identification methods ground their predictions on training corpora for a finite number of separate languages. In practice
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finite number of separate languages. In practice, the size of this language repertoire has varied from two to well over a thousand languages. Almost exclusively, the methods label whatever text
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biodiversity impact assessment methods considering multiple direct drivers of biodiversity loss 2) different ways to collect data for biodiversity assessment 3) experience in life cycle assessment and
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workshops with city partners. Coordinate travel, scheduling, and logistics for workshops in multiple Nordic cities (Helsinki, Espoo, Tallinn, Copenhagen). Collect stakeholder feedback during validation
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the Quantitative MRI research group at the Department of Technical Physics. The aim of the project is to develop and validate image reconstruction methods and hardware for low-field (~50 mT) magnetic resonance