77 distributed-algorithm-"Newcastle-University"-"Newcastle-University" Fellowship positions in Portugal
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- INESC TEC
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of virtual models (digital twins) for studying and detecting abnormal operating conditions (predictive diagnostics of anomalous behaviors) in distribution transformers using the finite element method (FEM
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artificial intelligence-based algorithms to optimise operation and predict anomalies in water distribution networks. The algorithms developed should identify patterns and anomalies that indicate the presence
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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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; T2: State-of-the-art review and model testing; T3: Development of algorithms for information extraction; T4: Algorithm testing and validation; T5: Preparation of reports, presentations, and other
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and optimisation algorithms, focusing on their practical application in the context of the RaceEngineerAI project. Tasks include: - Developing models capable of simulating the behaviour of racing
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algorithms; Minimum requirements: - experience with cross-platform mobile development frameworks (Ionic); - experience in software development using the Python programming language. 5. EVALUATION
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speed, classifying riding styles (e.g. conservative or more aggressive). Next, using inputs such as terrain type and slope, a decision algorithm based on Fuzzy Logic, decision trees or a lightweight
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leveraging on AI and optimization, applying data science and analytics techniques. Such tools will support the integration of Distributed Energy Storage (DES) and Distributed Energy Resources (DER) in
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degree in the field of bioinformatics and computational biology. Work plan: To develop and evaluate a hybrid quantum–classical approach for NGS data alignment, combining Grover’s algorithm with classical
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resource utilization, leading to performance losses and excessive energy consumption. The problem is exacerbated in distributed environments, where hundreds or thousands of GPUs operate suboptimally