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performance, yet their atomic-scale origin and role in reactivity remain poorly understood. The project addresses this open problem by integrating high-throughput Density Functional Theory, machine-learning
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and modelling of omics, clinical and imaging data, development of reproducible pipelines, application of machine learning techniques, integration of multi-modal data, scientific publication and
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Artificial Intelligence, Machine Learning, Computer Science, Telecommunications, or equivalent degree. Minimum of 3 years of postdoctoral research experience. Proven experience in various AI methodologies
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Website https://jobs.vhir.org/jobs/7377061-postdoctoral-researcher-stroke-research Requirements Research FieldBiological sciences » BiologyEducation LevelPhD or equivalent Skills/Qualifications Education
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of Barcelona; Particle Physics Phenomenology group. Main responsibilities / tasks: 1. Develop anomaly detection methods using Machine Learning and Simulation-Based Inference for high-dimensional parameter spaces
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software. (0-35) Experience in the application of advanced machine learning techniques (e.g., graph neural networks, reinforcement learning, probabilistic models, or latent representations) to biomedical
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the interplay between mutations, energetics, and evolutionary constraints, including epistatic effects. · Developing or applying machine learning approaches to predict or redesign frustration patterns in proteins
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in resulting companies, etc. The work will comprise machine learning research for analysing large-scale clinical data, including time-series physiological data, blood test data, medications
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. Recognised Researcher position has been opened. The ideal candidate holds a master's-level background in robotics, AI or related fields, with strong Python/C++ skills and experience in machine learning
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or equivalent Skills/Qualifications Technical Skills: Programming and integration of machine learning algorithms, reinforcement learning and symbolic planning in real robotic platforms. User modeling techniques