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. You have a strong interest in machine learning/artificial intelligence. You have experience with deep learning. You have experience with reinforcement learning (preferred). You have experience with
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attention to quality, integrity, creativity and cooperation. You have a strong interest in machine learning/artificial intelligence. You have a strong interest in graph-based learning (e.g., graph neural
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Inria, the French national research institute for the digital sciences | Montbonnot Saint Martin, Rhone Alpes | France | 2 days ago
and Objective: Tabular and Time-series foundational models have become very popular due to their high accuracy relying solely on In-Context Learning (ICL) [3, 4]. However, one still requires training
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on information theory, machine learning, and control to analyse how local variability, sensor drift, and platform differences affect both global model performance and human supervisory factors such as workload and
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electric machines for future defence and aerospace propulsion systems. The research will explore innovative machine topologies and advanced multi-physics design methodologies to enable electric machines with
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move beyond conventional stimuli-responsive materials, which merely switch between predetermined states, toward life-like materials that genuinely learn: Forming memories, consolidating them over time
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learning methods for medical image analysis, with a particular focus in anomaly detection and unsupervised learning. In this position, you will have the chance to explore basic machine learning research as
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disabled persons or persons of equal status will be given priority in accordance with SGB IX. The above position is one of 15 doctoral candidate positions of the MicroMan4Health doctoral network (https
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systems and control, applied mathematics, engineering, or a related field A strong background or interest in systems and control, applied mathematics, machine learning, and affinity with biological systems
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PhD studentship: Discovery of rational therapeutic biomarkers in breast cancer by systems pathology and deep learning Supervisor: Dr Hamid Raza Ali Department/location:Cancer Research UK Cambridge