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benefit from health insurance, supported by INESC TEC. 2. OBJECTIVES: Research and develop novel reliable deep learning computer vision algorithms for the detection and quantification of GIM lesions
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TEC. 2. OBJECTIVES: Collaborate with clinical partners in data collection and annotation Design and implement new deep learning solutions for the analysis of heart sound auscultation, electrocardiogram
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TRAINING: Literature review on anomaly detection in network data; Using deep learning to detect anomalies in network data flows.; 4. REQUIRED PROFILE: Admission requirements: Degree in Computer Engineering
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PROGRAMME AND TRAINING: - extend the knowledge of the state of the art in machine learning for lung cancer imaging data; - identify and select the appropriate methods for the study in question; - develop
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. Specifically, the work to be developed should propose a new design and implement a proof-of-concept prototype for an efficient and persistent write-ahead logging system for key-value stores. The proposed
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optimizations, such as new data caching algorithms. Develop a prototype that integrates the optimizations and experimentally evaluate the prototype.; Integrate the optimizations with a new modular and flexible I
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storage devices; - Implementation of a prototype of the previous system and design of profiling mechanisms that enable identifying complex I/O patterns and the corresponding placement rules; - Experimental
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with heat pumps/HVAC and EMS/BMS/SCADA, specifying data exchange and developing APIs; • support prototype tests, calibration and KPI definition (efficiency, cost, CO₂); • attend a transversal competences
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consumption over large-scale infrastructures; - Implementation of an experimental prototype and comprehensive evaluation in real scenarios (e.g., supercomputers).; ; The tasks described in this working plan
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of a prototype that includes the new content generation features, as well as traditional features of these tools (e.g., different access patterns, operation types, request sizes).; Evaluation