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the state of São Paulo (Brazil), using Light Detection and Range-LiDAR profiling data covering the entire state. LiDAR technology will enable a detailed analysis of forest structure, while deep learning
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data (PET, CT, Magnetic Resonance Imaging with Late Gadolinium Enhancement – MRI-LGE) and clinical variables. The approach encompasses unsupervised multimodal registration, three-dimensional deep
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position within a Research Infrastructure? No Offer Description Activities The fellow will be expected to research the relationship between these technologies (big data, machine learning, and the entire
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. Familiarity with frameworks such as TensorFlow and Keras, as well as libraries including Scikit-learn, NumPy, and pandas; - Experience with machine learning models such as Extreme Learning Machine (ELM
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; • Electrical characterization through current–voltage (I–V) and capacitance–voltage (C–V) measurements at deep cryogenic temperatures (< 4 K); • Optical characterization by photoluminescence (PL) spectroscopy
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. Requirements: PhD completed less than 7 years ago in Computer Science or related areas; experience in machine learning and data science (supervised/unsupervised models, recommendation and evaluation/robustness
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describing their progress and activities. Mandatory requirements Candidates must hold a PhD in Mathematics Education or a closely related field by the start of the fellowship. How to apply Applicants must
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implementation of processes for advanced treatment of effluent from different wastewater treatment plants under sunlight with the goal of water reuse. Mandatory requirements: PhD in Chemistry, Chemical Engineering
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therapy, in in vitro and in vivo contexts. Mandatory requirements: PhD obtained no more than 7 years ago in the field of Sciences; solid experience in cell culture and experimental assays, including protein
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level or within the Graduate Program. Mandatory requirements i) Applicants must have completed their PhD no more than seven (7) years prior to the application and must present an outstanding academic