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, accurate, and physics-informed machine learning models for predicting blood flow in patient-specific vascular geometries. Current simulation-based approaches require complex 3D meshes and are often too slow
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23 Jan 2026 Job Information Organisation/Company Rīga Stradiņš University Research Field Medical sciences » Health sciences Computer science » Modelling tools Researcher Profile First Stage
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Description Context Federated learning (FL) enables models to learn from distributed datasets across diverse clients (e.g., edge devices, hospitals, or industrial sites) while maintaining privacy [1]. A major
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Scientist will perform routine and complex mathematical modeling, machine learning, AI, computational, and statistical procedures to answer a variety of questions focused on direct clinical applications
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. Previous experience with machine learning applications in molecular modelling, including experience with at least three of the following Python libraries: TensorFlow, PyTorch, JAX, RDKit. Previous
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automates building and modifying surface structures, submitting DFT calculations, post-processing electronic structure and vacancy energies, and extracting machine-learning descriptors for modeling oxygen
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the beginning and there is still much to be learned! You will lead a project that centers on how tactile end organs assemble, function, and recover after injury. You will be using non-standard animal models
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design next-generation computer architectures for running large AI models on embedded and edge systems under strict timing, energy, and memory constraints. You’ll explore hardware-aware optimization and co
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algorithms and routines for image processing, image reconstruction and enhancement, deep learning model training and inference, explainability/visualization, and statistical analysis of AI performance. Conduct
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, Neuroscience, or a related field by the start date. Demonstrated expertise in computational modeling of human behavior or computer vision / machine learning. Proficiency in Python, MATLAB, or R. Strong