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- Delft University of Technology (TU Delft); yesterday published
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- Delft University of Technology (TU Delft); 17 Oct ’25 published
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. In this PhD project, you will: Develop real-time optimization and hybrid AI models for end-to-end multimodal transport planning under uncertainty. Design synchronization, consolidation, and matchmaking
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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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of battery modelling and algorithm development, with a strong emphasis on the data-driven modelling and control aspects. You will contribute to shaping the technologies that underpin a more sustainable and
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algorithms to ensure seamless, reliable, and secure wireless communication in challenging and dynamic environments. The key responsibilities for this positions are listed as the following: Develop protocols
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exploited by algorithms, leading to efficient solvability. Due to the development of such algorithms, structured integer programs play a critical role in many decision-making processes leading to improved
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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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responsibilities include: Development of a flood classification framework for flood type prediction Comparison of different ML algorithms in a sensitivity study Communication with stakeholders Development of open
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website: http://www.esa.int Field(s) of activity for the internship Topic of the internship: Algorithmic Optimisation of Stowage for a Cargo Return Vehicle You will help develop a numerical optimisation
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these technologies can only read DNA fragments of limited length. We enable biological interpretation of these sequencing data sets by developing algorithms based on graph theory, discrete optimization and machine
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research is developing structured, LLM-readable document representations that enhance accuracy, facilitate automation, and improve interoperability across different model types used within the organization