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- Delft University of Technology (TU Delft)
- Delft University of Technology (TU Delft); yesterday published
- Vrije Universiteit Amsterdam (VU)
- Delft University of Technology (TU Delft); 17 Oct ’25 published
- Delft University of Technology (TU Delft); today published
- Eindhoven University of Technology (TU/e)
- Erasmus University Rotterdam
- University of Amsterdam (UvA)
- University of Amsterdam (UvA); Published today
- Utrecht University
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Field
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participants of the Netherlands Twin Register, integrating genetic and psychological data where relevant. Beyond algorithm development, you will also address methodological challenges such as data quality, bias
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to source localization based on microphone arrays or distributed sensors. This PhD project will focus on the development of novel methods and algorithms for airborne noise source localization in generic urban
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; Develop system architecture and training strategy to enable the FM to learn from heterogeneous MRI data in terms of data source purpose and physical location in the scanner; Develop efficient techniques
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students to become experts in a specific domain of choice. This vacancy is explicitly targeted at candidates interested in algorithmic biases and developing methodological approaches to tackle this challenge
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or incomplete. Information Your tasks will include: Developing and benchmarking ML/AI algorithms tailored to low-data regimes — e.g. few-shot learning, transfer learning or data-efficient representation learning
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of both science and societal application. We contribute to innovative information technologies through the development and application of new concepts, theories, algorithms, and software methods. With our
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tagging algorithm development as well as physics data analysis, with a focus on Higgs boson physics, top quark physics, and searches for new physics signatures. This is what you will do After the discovery
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(FELIX) for ATLAS detector systems. The group also has a strong record in track reconstruction, flavour tagging algorithm development as well as physics data analysis, with a focus on Higgs boson physics
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to test and demonstrate the developed concepts and algorithms for integrated (re)planning. This PhD research will use a mixture of techniques from logistics, operations research, multiple-criteria decision
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this PhD project, you will investigate the co-design between event-based learning algorithms and neuronal hardware units with multi-scale time constants. The algorithmic methodology will exploit recent