32 molecular-modeling-or-molecular-dynamic-simulation Fellowship positions at INESC TEC
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the modelling and control of microgrids Previous experience in real-time simulation and Power Hardware In the Loop test systems. At least one paper in conference or journal. Minimum requirements: Solid experience
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of the institution's charging infrastructure.; • Contribute to the development of energy metering modules.; • Explore and implement software solutions for communicating metering data between devices.; • Design, simulate
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, energy consumption, and accuracy.; ; Training deep learning models, especially in LLMs, faces critical challenges that compromise the optimal use of GPUs. These bottlenecks result in poor computational
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learning models for generating artificial data using generative models. The result will be high-fidelity medical data. 3. BRIEF PRESENTATION OF THE WORK PROGRAMME AND TRAINING: - extend the knowledge
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: - prepare the requirements specification for a software module that allows the use of pre-trained large language models (Large Language Model); - containerization and availability of trained models
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Education Institutions. Preference factors: - Knowledge of fundamental concepts related to energy management and gas networks; - Knowledge of optimization and forecasting models; - Knowledge of Python
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designs and results 3. BRIEF PRESENTATION OF THE WORK PROGRAMME AND TRAINING: -Modelling electricity markets ; -Modeling energy resources planning ; -Integration of resources for self-consumption
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:; Phase 1 - Development of Graphical Interfaces; - Design and development of dynamic graphical interfaces for 3D visualisation of packaging solutions; - Implementation of interactive features
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experiments to simulate submarine cable deformations and study their effects on signal phase and polarization.; ; 3. BRIEF PRESENTATION OF THE WORK PROGRAMME AND TRAINING: This research project focuses
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of this project is to create a radiomics and radiogenomics based approach to describe and create predictive models to characterize lung cancer based on a non-invasive methodology. 3. BRIEF PRESENTATION OF THE WORK