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the resilience, safety, and security of critical infrastructures as core requirements. Moreover, it will boost the development and validation of novel AI algorithms, by the European AI community, through open
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Dipartimento di Ingegneria dell'Informazione - Università degli studi di Padova | Italy | 2 months ago
. The proposed project aims to develop new techniques for observability analysis and distributed state estimation directly from data, even in the presence of noise, disturbances, and unmeasured inputs
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knowledge by applying statistical, algorithmic, mining and visualization techniques. Data Strategy and Innovation plays a critical strategic role within Advancement, providing the analytical framework, data
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the position are: Integration of multimodal sensors (e.g., physiological, kinematic and behavioural signals) into a driving simulator for patient assessment. Development of algorithms for the analysis
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processing algorithms Development and implementation of novel pulse sequences Acquisition of high quality NMR data Interaction with other research groups, learning new techniques where necessary Close
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to develop a strong profile as researcher. You will also gain valuable experience that might be relevant for your further career. The purpose of a postdoctoral position is to build up a researcher profile that
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Apply online now: https://karriere.klinikum.uni-heidelberg.de/index.php?ac=application&jobad_id=28417 PhD student (m/f/d) with a focus on epidemiology and tools for screening of Tuberculosis
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mission in society Developing tasks in a strong and international professional environment Career guidance and follow-up during the PhD period Open and inclusive working environment with committed
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execution and natural language justification of automated decisions. Work plan: Phase 1: Data Preparation and Architecture (Preparation); Phase 2: Modeling and Algorithms (Analytical Development); Phase 3
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of the postdoctoral fellow will include: conduct scientific research in the field of machine learning. develop novel machine learning algorithms primarily for representation learning, dimensionality reduction