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Field
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, which will be funded and mentored by Prof. Joseph Byrnes. The primary project will be an NSF-funded collaboration with Ryan Porter and Jim Gaherty at Northern Arizona University to investigate
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The Data Integration in the Life Sciences group, led by Prof. Dr. Katharina Baum, is seeking a motivated student assistant to contribute to the cutting-edge, BMBF-funded research project Act-i-ML
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-driven methods to include prior information about the underlying system Addressing process specific nonlinearities and system-theoretic properties to develop novel control and machine learning algorithms
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challenge meeting this requirement is the simultaneous need for low-power consumption. The main objective of the project is to develop a complete end-to-end high-performance DNN system for on-premise
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associate will conduct theoretical studies and numerical simulations, develop new methods and algorithms, and mentoring junior group members. Physical Demands and Working Conditions Physical Activities
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application. The successful candidate will work at the intersection of multi-disciplinary modelling, advanced AI algorithms, and decision-support tool development for various hydrogen technologies-based energy
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learning, and AI-driven manipulation. This position offers the opportunity to work on real-world robotic systems and develop novel algorithms at the intersection of robot learning, control, and AI
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underlying system Addressing process specific nonlinearities and system-theoretic properties to develop novel control and machine learning algorithms for edge computing Active collaboration with our industrial
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: Develop an event-driven RL algorithm that sparsely updates network state and parameters that will significantly improve energy to-solution efficiency compared to conventional digital accelerators when
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developed by the project partners will be based on two key technologies: machine learning algorithms that generate artificial yet realistic data points (synthetic health data) and secure multi-party