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The Data Integration in the Life Sciences group, led by Prof. Dr. Katharina Baum, is seeking a motivated student assistant to contribute to the cutting-edge, BMBF-funded research project Act-i-ML
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computing, domain-specific multi- and manycore architectures, networks-on-chip (NoCs), methods and algorithms for application parallelization, simulators and virtual platforms for application- and
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terrestrial system models, for example using data analysis methods, such as data assimilation, physical- or process-based machine learning, or deep learning algorithms Analysis of the effects of human
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We are the research groups “Tectonics and Sedimentary Systems” of the Institute of Geological Sciences at the Freie Universität Berlin under the direction of Prof. Dr. Alexander Webb and Prof. Dr
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developing a machine learning (ML) algorithm for the automated analysis of the above-mentioned mass spectra. Desirable: - knowledge in the field of Planetary Sciences - very good written and spoken English (C1
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implementation, practical application, theoretical analysis and evaluation of AI algorithms Use of XAI tools to explain machine learning models Implementation of deep learning Improvement of models, e.g. in terms
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students who would like to write their final thesis in the field of machine learning / computer vision. The primary goal of this master’s thesis is to develop an algorithm that can accurately and efficiently
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-omics data sets generated with innovative high-throughput technologies used in Research Sections I and II (e.g. sensory, metabolome, proteome and transcriptome data) by using efficient algorithms and
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efficient algorithms and machine learning/artificial intelligence methods in combination with complex network analysis tools to predict and model interactions between food and biological systems • Further
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Computational Molecular Medicine, led by Prof Julien Gagneur, develops computational approaches to study the genetic basis of gene regulation and its implication in diseases. Applications of our work range from