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transport, supply chain and logistics modelling to contribute to the development and use of the MILES (Multimodal Integrated Logistics for Simulation) and MATRA (Multi-Agent Transport Resilience and
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to the development of novel indoor localization and tracking methods, algorithms, and systems ? Evaluating the performance of such methods, algorithms, and systems via modeling and simulation ? Performance evaluation
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harmonization, uncertainty characterization of existing metrics, algorithmic improvement for ET and GPP products, development of novel remote sensing products. Leveraging remote sensing for societally relevant
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to support major transitions, train responsible engineers, and place scientific and technical excellence at the service of education, research, and innovation. As part of the France 2030 Superviz project
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are: i) to develop a new selected CI algorithm allowing reaching chemically-accurate results for large compounds; ii) to extend the currently-available database to properties relevant for both core
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responsibilities will span all stages of research, including collecting data of in both tabular and spatial formats, developing algorithms that clean and organize data, conducting statistical analyses, running
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microcontrollers to coordinate hardware/software systems. Adapt existing machine vision algorithms to extract fly behaviors in real time and offline. Develop computational tools and analysis pipelines for processing
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, approximate length 2-4 pages; short-listed candidates will be asked to prepare full-length portfolios at a later stage according to Aalto University’s instructions: https://www.aalto.fi/sites/default/files/2024
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(algorithms), and statistics. During this project, you will develop new methods to construct phylogenetic networks and generalize mathematical frameworks of phylogenetic network classes to tackle related
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of the observation receiver used to measure the transmitter output and extract distortion information. This position is part of the ERC Synergy DISRUPT project, which aims to develop new architectures for observing