40 molecular-modeling-or-molecular-dynamic-simulation Fellowship positions at INESC TEC
Sort by
Refine Your Search
-
Listed
-
Field
-
interfaces (HMI), and industrial-grade communication protocols for automation in electric power systems.; • Develop and adapt a test network — a simulation model or a replica of a real network — for DIgSILENT
-
computational simulation models / MATLAB/Simulink.; Knowledge of industrial-grade communication protocols (Modbus TCP, IEC 61850, etc.). ; Minimum requirements: Educational background in Electrical Power Systems
-
courses of Higher Education Institutions. Preference factors: • Experience with computational simulation models / MATLAB/Simulink.; • Knowledge of industrial-grade communication protocols (Modbus TCP, IEC
-
workload’s data (e.g., Deep Learning, Large Language Models) while addressing the I/O interference and fairness challenges faced by current distributed infrastructures, where storage resources are being shared
-
study cycle or non-award courses of Higher Education Institutions. Preference factors: • Experience with computational simulation models / MATLAB/Simulink.; • Knowledge of industrial-grade communication
-
developing synthetic datasets using simulation environments. Familiarity with the theory and implementation of diffusion models and GANNs applied to the same context. Minimum requirements: • Knowledge
-
Institutions. Preference factors: • Experience with computational simulation models / MATLAB/Simulink.; • Knowledge of industrial-grade communication protocols (Modbus TCP, IEC 61850, etc.). ; Minimum
-
the fact that security and data breaches in AI systems can progressively affect the quality of future decisions - Development of a simulation system that allows, through a disturbance agent, the
-
the fact that security and data breaches in AI systems can progressively affect the quality of future decisions ; - Development of a simulation system that allows, through a disturbance agent, the
-
with simulation techniques, energy efficiency models, large-scale energy consumption data, machine learning techniques and interpretation (unsupervised); - Education, experience and research orientation