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domains. In the advertised position, focus of the modelling will be on coupled hydrodynamical-wind waves and sediment modelling. This work will contribute to the development of Digital Ocean Twins fostering
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) - Strong experience with advanced analysis methods for fMRI or EEG data (e.g., decoding, representational similarity analysis, deep neural network modeling) - Excellent programming skills in Python or Matlab
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us as a full-time Research Associate / PhD candidate as part of the TUM GNI-funded research project VAULT-AI . Position overview Do you have a passion for digital construction technologies and enjoy
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Max Planck Institute of Molecular Cell Biology and Genetics, Dresden | Dresden, Sachsen | Germany | about 1 month ago
at the interface of the life sciences. In this key position, you will ensure the scientific and operational success of a dynamic group by managing collaborative research projects and grants, coordinating scientific
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experimental planning, bioinformatic consulting, and in-depth data interpretation, where applicable Your qualifications Master’s degree or PhD in Bioinformatics, Computer Science, Applied Statistics, or a
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the research area of adversarial robustness in LLMs as a Doctoral candidate / PhD Student (f/m/d) At the chair of Data Analytics and Machine Learning at the Technical University of Munich (TUM), a full position
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collaboration across various disciplines and domains. In the advertised position, focus of the modelling will be on coupled hydrodynamical-wind waves and sediment modelling. This work will contribute
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RNA-seq datasets and generate user-friendly applications for data exploration; to perform data analysis derived from diverse scientific topics in collaboration with PhD students and postdocs; to train
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Information and Knowledge Engineering: (Co-)Development of domain-specific vocabularies, ontologies and metadata schemas in collaboration with colleagues across the HMC Organization and integration of metadata
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analysis tools Implementing continuous benchmarking of the library Optimization of the library algorithms and underlying data models for speed and memory efficiency Maintenance and improvement