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- Universitat Politècnica de Catalunya (UPC)- BarcelonaTECH
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Field
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measurements Calibration of materials as sensors to measure temperature, oxygen, and pH values in cells Development of models based on artificial intelligence algorithms to interpret luminescence signals Study
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implementing algorithms based on online Sparse Gaussian Processes and advanced probabilistic techniques enabling AUVs to dynamically alter their trajectories, cutting down on uncertainty and improving efficiency
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-off companies. CONTEXT AND MISSION We are seeking a postdoc to join the Quantum Machine Learning team (QML-CVC) in beautiful Barcelona. The QML-CVC team (https://qml.cvc.uab.es /) is part of
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. SILEX 2025) to calculate the Fire Radiative Power (FRP) and compare with satellite observations (VIIRS, SLSTR, FCI). Develop a fire front segmentation algorithm using machine learning techniques (deep
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to facilitate perceptual learning of different stimulation patterns; and (iii) the development of advanced AI algorithms capable of converting camera input into real-time electrical stimulation parameters. In
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the development and/or implementation of algorithms and/or computational pipelines Background/experience in building statistical and/or machine learning methods, in particular for data integration tasks
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articles. Where to apply Website https://seuelectronica.upc.edu/en/procedures/call-for-recruitment-of-pdi-postdo… Requirements Research FieldPhysics » OpticsEducation LevelPhD or equivalent Skills
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Infrastructure? No Offer Description Research line: Learning in single cells through dynamical internal representations. Job description: Develop theory, models and algorithms for identifying molecular encodings
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of auditory stimuli and creation of stimulation sequences 5) Implementation of pilot studies in adults and infants 6) Programming analysis algorithms for FFR, MMN and statistical learning, based on spectral and
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pathways, including deactivation processes. Screening and fine-tuning catalysts to enhance performance. Developing workflows and machine learning algorithms to accelerate catalyst design (optional). Group