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Dipartimento di Ingegneria dell'Informazione - Università degli Studi di Padova | Italy | 28 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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: 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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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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, 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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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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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 successful candidate will be able to teach one of the undergraduate courses related to AI/ML, programming languages, data structures and algorithms, operating systems, network security, visualization, and
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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
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integrating artificial intelligence, from algorithm design to on-sky demonstration. The objective is to design intelligent adaptive optics systems capable of correcting sensor nonlinearities, anticipating