Study and development of hardware design for image recognition using neural networks.

To conduct research and development of parallel processing and high-performance computing infrastructures. To support research and tech-nical demos. To research and develop distributed and parallel storage systems, operating systems, distributed schedulers, software man-agement, and other related matters.

The candidate will work on Tasks 2-3 of the project, addressing the problem of model-based reinforcement learning in non-stationary environments using deep learned models. The key objectives are: 1. Conduct an extensive survey of state-of-the-art on model-based reinforcement learning, learning latent dynamics using deep models, and planning using learned models. 2. Develop new methods for learning disentangled latent representations for dynamic processes with multiple operation modes. 3. Extend MCTS planning algorithms to handle the models learned in 2.

Ainur - PTDC/CCI-COM/4485/2021 - BI|2022/244 The objective of this research grant is to conduct preliminary research on instrumentation techniques for Persistent Memory systems. The activities will be conduce in the context of the Activity "Instrumentation and Fault Injection". The work plan therefore includes studying existing target systems and tools, and developing preliminary instrumentation techniques guided by a simple use case.

Ainur - PTDC/CCI-COM/4485/2021 - BI|2022/245 The objective of this research grant is to conduct preliminary research on fault-injection techniques for Persistent Memory systems. The activities will be conduce in the context of the Activity "Instrumentation and Fault Injection". The work plan therefore includes studying and developing preliminary fault injection techniques guided by a simple use case.

OBJECTIVES/WORKPLAN 1) Develop software Network Functions for the Tofino Programmable Switch Platform 2) Develop data-structures modules using the Tofino Programmable Switch Platform for the SyNAPSE framework