The aim of this project is to build a desktop tool to be used to analyse and visualize Turkish maqam music pieces. This is an engineering project, it does not involve any research. The skills required are python and docker.
The idea is to convert the models to logic formulas (we already have algorithms for this), feed to formulas to the OptiMathSat tool, retrieve the solution and visualize it back on the model.
In this project you are expected to implement a serverless computing framework for running applications on the edge.
Serverless computing typically allows its users to define the application in terms of functions, events and triggers. In this context, developers are not explicitly aware of the computational infrastructure details such as VMs or containers. Although, at first sight this means a more frictionless development effort, serverless computing is not without its technical challenges. One example challenge is the cold-start problem and there are others as well.
Online games where many online players share a common world are fun to play but also they present many technical challenges. Typically, the game universe with all the players on it would not computatinally fit into a single game server.
Automated Machine Learning (AutoML) consists of stages to automate the entire pipeline of machine learning. AutoML is especially useful for teams/organizations that does not necessarily have sufficient expertise on the AI/ML domain but expected to implement practical ML applications based on given data sources. There are already a variety frameworks for AutoML such as H2O, TPOT, Auto-SKLearn and AutoGluon.
Neural Architecture Search (NAS) is a method fpr automating the process of designing neural network architectures by using algorithms such as evolutionary algorithms or reinforcement learning to search through a predefined space of architectures. NAS is used by a couple of open-source AutoML tools such as AutoKeras, AutoPytorch and AutoGluon in order to find a DNN model with best performance for a given task.
In this project, you will work on cloud-native development and deployment of robotic applications. As the case study, we will use the Autoware repository (https://github.com/autowarefoundation/autoware) and apply cloud-native concepts and tools to build, test, and deploy Autoware in various containerization settings and granularity. You will get practical experience in using Docker, Ansible, and ROS2 tooling.