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Field
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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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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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of dynamic physical systems and control system algorithms experience in software development, data analysis and AI practical experience in experimental work motivation and potential for research within
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setting, enabling fast and predictable adaptation with minimal overhead. A central focus is the co-design of algorithms with edge hardware and embedded platforms. You will investigate implementation
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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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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
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will also collaborate with a postdoctoral researcher and another PhD candidate on creating new GPU-enabled pharmacophore searching algorithms. Prospective validation will be achieved by predicting
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on practical feedback linearization with limited or imperfect models. Learning-enabled control dynamics Embedding optimization and learning algorithms (e.g., SGD, Bayesian updates) into control design and
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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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Associação do Instituto Superior Técnico para a Investigação e Desenvolvimento _IST-ID | Portugal | about 1 month ago
. The candidate(s) may also be required to apply data fitting algorithms/machine learning algorithms to link models to biological data from the literature. The project integrates elements from dynamical systems