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Dipartimento di Ingegneria dell'Informazione - Università degli Studi di Padova | Italy | 30 days ago
to apply Website https://www.dei.unipd.it/bandi Requirements Additional Information Eligibility criteria Eligible destination country/ies for fellows: OTHER Eligibility of fellows: country/ies of residence
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focuses on modeling IoT-Fog environments, designing multi-objective optimization algorithms (latency, energy, reliability), and developing strategies for critical IoT applications like smart cities and
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, from the quantum processor to the quantum-classical interface and all the way quantum algorithms and applications. Further information on the Department is linked at https://www.science.ku.dk/english
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applications for its 2026–2027 Herman Goldstine Memorial Postdoctoral Fellowship for research in the theory of algorithms and algorithm design. The fellowship provides scientists of outstanding ability
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: The objective of this task is to develop a decision-support model to assist in the selection of diagnostic and prognostic algorithms by jointly optimizing energy and computational costs. Two goals are pursued: (i
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at the nanometer scale, in order to benchmark ISOM. On the computational side, the work will focus on the implementation of reconstruction algorithms and calibration methods.ES, regular (weekly) meetings will be
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, quantitative analysis, performing medium-scale microscopy experiments, Python or Matlab programming, synthesizing information from the published literature, and use and development of machine learning algorithms
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dedicated to investigating the world of TEs by creating innovative genomic algorithms, adapting the latest sequencing technologies, and deploying cutting-edge experimental facilities. The lab studies TEs
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during surgery or endoscopic exploration. This postdoctoral position aims at developing innovative deep learning algorithms to help histology classification. Both classical histology based on hematoxylin
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(LES) results. Key Responsibilities: Develop and refine numerical algorithms for real-time wind field forecasting. Validate forecasting models against high-fidelity LES data and field measurements