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experimental molecular biology and data analysis. Doctoral candidates can specialize in genomic and molecular biology techniques, as well as in algorithms, statistics, and artificial intelligence for molecular
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Description In the Department of Nuclear Medicine, we are interested in the development and clinical translation of molecular imaging tools for various applications, such as oncology, regenerative
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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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Data-Driven Autonomous Mobile Robotics for Aquatic Biodiversity Monitoring Shape the Future of Field Robotics You want to develop robotic systems that do not just work in the lab, but operate robustly in
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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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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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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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Your Job: Join our team as a dedicated scientist and contribute to our exciting research projects. Our work focuses on models and algorithms for supervised and unsupervised learning. We devise deep
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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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, and training methods - across multiple technological platforms - photonics, electronics, biological neurons. Responsibilities and tasks This PhD project aims to develop, verify, and benchmark learning