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biochemists developing the labeling agents, data analysts developing analysis algorithms and physicists developing hardware. The candidate The candidate should have a firm base in in vivo imaging and cell
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comparative embedded ethics approach, examining neuroAI development in both clinical neuroscience and neurorobotics settings. Your tasks: The postdoctoral researcher will play a central role in the project and
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“Stability and Solvability in Deep Learning”. This project focuses on mathematically analyzing machine learning algorithms with a particular focus on questions of stability, computability, and robustness
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Planning of and participation in (RMT) field experiments in Germany, Europe and worldwide Further development of the processing algorithm for RMT data and integration into the analysis software available
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) and the University of California Irvine (UCI). The Research School "Foundations of AI" focuses on advancing AI methods, including energy-efficient and privacy-aware algorithms, fair and explainable
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, and unmodeled dynamics remains a key challenge. This position focuses on developing and validating methods that jointly address safety, performance, and reliability of learning-based control and
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(e.g. RNAi, CRISPR/Cas9, small-molecules). In this context, we also develop new computational tools for automated analysis and data visualization. These include algorithms and software applications
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be used for the modeling and simulation of materials. What you will do As a research associate, you will work on research and development tasks in the field of quantum computing algorithms
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neurons. Responsibilities and tasks This PhD project aims to develop, verify, and benchmark learning rules in networks of complex spiking neuron models in the application field of geolocalization: Building
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acceleration of large-scale machine learning workloads Perform characterization and modeling of electronic and optical devices Develop hardware-aware machine learning models incorporating electronic and optical