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Field
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dynamics; Explainable AI: With a particular emphasis on mechanistic interpretability. Invent, evaluate, and publish novel algorithms, aiming for theoretical guarantees when working with structured and
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- manufactured Ni₃Al intermetallic alloys that integrates residual stress considerations directly into the design process. The research will focus on the development of topology optimization algorithms capable
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Inria, the French national research institute for the digital sciences | Villers les Nancy, Lorraine | France | 10 days ago
incremental and constraint-preserving replication mechanisms. Extend replication semantics beyond raw data by introducing algorithms and protocols that propagate and merge views while preserving convergence
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learning algorithms. Personalizing user interactions by building models that adapt explanations to specific knowledge levels and interests of users, so that user modelling and formal reasoning transform
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& Collaboration The successful candidate will work at the interface of machine learning and biostatistics, developing new theory, algorithms, and scalable implementations. By establishing a new class of multi-frame
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dynamics; Explainable AI: With a particular emphasis on mechanistic interpretability. Invent, evaluate, and publish novel algorithms, aiming for theoretical guarantees when working with structured and
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features from multiple imaging modalities (CT, MRI, PET, ultrasound); (2) design advanced AI algorithms for early-stage cancer detection with high sensitivity and specificity; (3) create user-centric AI co
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Learning with Graphs led by Prof. Nils M. Kriege. Our research focuses on the development of new methods and learning algorithms for structured data. Graphs and networks are ubiquitous in various domains
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of algorithmic systems. The research will investigate how clinicians interact with automated and machine learning–based decision-support systems, with a particular focus on cognitive workload, trust, situational
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network integration for emerging low-energy opto-electronic AI systems and beyond. The challenge: Machine learning and neural networks are super-charging the complexity of problems that computer algorithms