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city models focused on health and environmental infrastructures. Advanced Data Analysis: Advanced skills in machine learning, deep learning, and advanced statistics for processing complex data. Urban
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Epic or eClinicalWorks. 2 years of experience developing machine learning models (e.g., random forests, gradient boosting, neural networks) for healthcare or clinical research applications. 1 year of
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clinical features using machine learning and foundational modeling approaches. This work supports disease modeling across chronic kidney disease, acute kidney injury, cancer, and neurological conditions. A
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Hybrid Crop Modelling Framework, integrating Process-Based Models (PBMs) with Machine Learning (ML) to enhance the accuracy and interpretability of crop yield forecasts, while evaluating key ecosystem
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at demonstrating how teacher learning can be made robust in an event-based framework, that is, when both the teacher model and the learning rules are event- based. The combination of excitable teacher dynamics and
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-based knowledge with machine learning. You will work closely with the Utrecht University team and OpenGeoHub together with other project partners, to develop and implement surrogate and hybrid modelling
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Michael Bronstein, AITHYRA Scientific Director AI and Honorary Professor of the Technical University of Vienna in collaboration with Ismail Ilkan Ceylan, expert in graph machine learning, invites
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and will collaborate closely with clinicians, industry partners, and research teams at Heriot-Watt University and the University of Aberdeen. Key Duties and Responsibilities Machine Learning Model
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experience in: Deep learning Medical imaging computing (preferably neuroimaging) Computationally efficient deep learning Deep learning model generalisation techniques. Translating deep learning models
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support productivity workflows, guided by emerging large language models. Candidates ideally have a background in Computer Science and prior experience with human-computer interaction (e.g., through courses