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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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, medical informatics, databases, data mining, machine learning, applied mathematics, biomedical modelling and analysis of complex networks. Joint data science projects between the different partners
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of AI-supported coding assistance functions (e.g., suggestion models, highlighting of relevant text passages, active learning strategies) to accelerate coding while ensuring high quality. Systematic
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). These collaborations enable practically relevant and breakthrough results. This team goal requires a quantitative model describing and predicting sperm motility under various conditions. You will develop the digital
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Leibniz-Institute for Food Systems Biology at the Technical University of Munich | Freising, Bayern | Germany | 3 months ago
the form of graphs to analyze and predict food-effector systems. Key Responsibilities Develop Probabilistic Machine Learning Models to integrate graphs and food-related omics data Multi-omics integration
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Responsibilities • Develop Probabilistic Machine Learning Models to integrate graphs and food-related omics data • Multi-omics integration using graph-structured prior knowledge • Analyze food-related (multi-)omics
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and a heterogeneous emerging computer architecture, collaborate regarding compiler and other tools as well as modeling their hardware for integration into the emerging computer architecture framework
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qualified women. About the position The position contains both teaching duties and participation in research projects. The research project topics focus on improving object recognition through computer vision
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take place monthly. A lecture series on theoretical and experimental neuroscience as well as machine learning is addressed primarily to doctoral students. Lectures are held by principal investigators
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biological models. This position involves the optimization, and operation of an innovative multimodal microscope, as well as close collaboration with experts in the biological sciences for its application