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Field
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devices Develop hardware-aware machine learning models incorporating electronic and optical device constraints Design and implement hardware-efficient training methodologies for machine learning systems
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, graph neural networks, physics-informed ML) to approximate PF results Train models using simulation results generated from conventional power flow solvers Evaluate AI-based approximators in terms
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of research and innovation! Be part of change Design and development of IP cores for low-latency, high-throughput digital signal processing Simulation and verification of the implemented algorithms with test
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of the IMPRS reflects the development of molecular genetics into an information science, based on the plethora of experimental data that are nowadays available and steadily being produced about cellular
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robotics Goal-driven agentic AI Autonomous medical imaging Design of AI-enhanced medical devices Machine learning models and algorithms for medical signal processing Embedded AI Privacy-aware AI Foundations
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, and training methods - across multiple technological platforms - photonics, electronics, biological neurons. Responsibilities and tasks This PhD project aims to develop, verify, and benchmark learning
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processing, algorithm design, optimisation and simulation, software engineering and automation and control systems. An overview of the current PhD research projects is given here: https://www.dashh.org
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machine learning, deep learning, or AI. Solid mathematical, algorithmic, or physics background, distinct analytical skills. Very good programming (Python, C++) and computer (Linux, Windows) skills
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optimizing reaction conditions compared to human decision making and design of experiments techniques. We will develop a Bayesian optimization algorithm for the optimization of reaction yields
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Leibniz-Institute for Food Systems Biology at the Technical University of Munich | Freising, Bayern | Germany | 2 months ago
systems. Key Responsibilities Develop graph-based (multi-)omics analysis algorithms Benchmark graph-theoretic against graph-ML approaches Analysis of food-related (multi-)omics data Your Profile The ideal