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algorithms and data structures. Experience with AI frameworks and libraries (e.g., TensorFlow, PyTorch). Ability to develop and implement AI models for business applications. Knowledge of cloud computing
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will be able to teach one of the undergraduate courses related to AI/ML, programming languages, data structures and algorithms, operating systems, network security, visualization, and human-computer
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data and machine learning processing pipelines. It will also focus on developing AI algorithms for semantically enriching data assets with domain knowledge, which feed into building knowledge graphs
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Responsibilities: Develop novel quantum reinforcement learning (QRL) algorithms using variational quantum circuits (VQCs), focusing on the multi-agent setting. Design and implement efficient credit assignment
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-resource settings. This project aims to achieve several objectives, including the development of a new AI-algorithm and a paired dataset for comparing how different imaging techniques influence
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an extensive safety analysis and calidation of perception algorithms in automotive. Through our work, we lay the foundation for a reliable digital future. What you will do In our Trustworthy Digital Health group
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nanocrystal morphology and optical property characteristics. The researcher will work collaboratively with graduate student(s) to design and implement a machine learning (ML) algorithm using the database as
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. By integrating cutting-edge multi-agent systems, federated learning, and game theory, this project will develop sophisticated decentralised algorithms that enable autonomous vehicles (AVs
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algorithms and program Building Automation System (BAS) computers in close coordination with the Director of Systems/Energy Engineering. Modify existing, and create new BAS object definitions, based on control
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techniques and the structure of bilevel problems in large-scale settings. Objectives The goal of this postdoctoral project is to develop scalable blackbox optimization algorithms tailored to bilevel problems