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learning algorithms (e.g., graph neural network (GNN) architectures) will be developed to explain the identified small-scale processes as accurately and efficiently as possible and to ultimately develop a
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and analysis of mathematical methods for novel imaging techniques and foundations of machine learning. Within the project COMFORT (funded by BMFTR) we aim to develop new algorithms for the training
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detectors (Partial) automation of detector characterization for more efficient analysis Algorithm development: Development of a correction method based on information field theory for atmospheric image
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multi-parameter ion-beam tuning procedures (collaboration with Univ. of Vienna and HZDR) and developments of machine learning (ML)-algorithms for optimization of beam parameters and control of relevant
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group focuses on developing strategies and algorithms to quantity biologic effects of particle radiation based on underlying physics, biology and physiology. Within the BMFTR funded project “BIOMICRO
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control, state estimation, and path planning algorithms for single and multi-agent robotic systems (UAVs). develop and train AI models for practical applications such as real-time object detection and
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Dortmund, we invite applications for a PhD Candidate (m/f/d): Analysis of Microscopic BIOMedical Images (AMBIOM) You will be responsible for Developing new machine learning algorithms for microscopy image
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applications. Our overarching aim is to obtain a holistic view of interconnected biological systems in health and disease. We develop clearing technologies for cellular-level imaging and deep learning algorithms
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of terrestrial systems analysis, we seek a candidate who can develop and lead future research activities in one or more of the following directions or related topics: Innovative observation methods for terrestrial
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is to investigate which antigen specificities are enriched in cell subpopulations, depending on the underlying neurological disease. The project will use high-throughput data to develop and apply