Sort by
Refine Your Search
-
Listed
-
Employer
- Nature Careers
- Technical University of Munich
- Fraunhofer-Gesellschaft
- University of Tübingen
- Free University of Berlin
- DAAD
- Forschungszentrum Jülich
- Leibniz
- Max Planck Institute for Intelligent Systems, Tübingen site, Tübingen
- Max Planck Institute of Immunobiology and Epigenetics, Freiburg
- TECHNISCHE UNIVERSITAT DRESDEN (TU DRESDEN)
- 1 more »
- « less
-
Field
-
macromolecular dynamics with statistical mechanics, molecular simulation at different resolutions, machine learning, and experimental data. Our group works on the definition and implementation of strategies
-
statistics, bioinformatics, machine learning and AI applications. Experience in a number of these technologies is expected. Collaborations within the Cluster of Excellence ImmunoSensation and with other intra
-
Dust Analyser onboard the Cassini space probe - Collaboration with a computer scientist who is developing a machine learning (ML) algorithm for the automated analysis of the above-mentioned mass spectra
-
such as ecology, economy and social sciences. ZMT aims to use data science tools, including computer vision and deep learning, for the study of rapid changes in tropical coastal socioecological systems
-
maintain pipelines for the analysis of high-throughput sequencing data, including RNA-seq, ChIP-seq, ATAC-seq, and single-cell and spatial omics. Integrate machine learning and large language models (LLMs
-
Your Job: Scientific and technical lead of a team focusing on machine learning and big data analytics in X-ray science Development and application of machine learning tools for X-ray data analysis
-
Max Planck Institute of Immunobiology and Epigenetics, Freiburg | Freiburg im Breisgau, Baden W rttemberg | Germany | about 2 months ago
of high-throughput sequencing data, including RNA-seq, ChIP-seq, ATAC-seq, and single-cell and spatial omics. Integrate machine learning and large language models (LLMs) into bioinformatics workflows
-
. Bringing together internationally leading expertise in Climate Modelling, Earth Observation, and Machine Learning, research at the center will advance our modelling capacity of the Earth’s climate and
-
), and computational modeling (deep neural networks). We apply multivariate analysis methods (machine learning, representational similarity analysis) and encoding models. Job description: This is an open
-
multimodal datasets Design and fine-tune machine learning and deep learning models to extract meaningful patterns and predict metastatic behavior Collaborate closely with experimentalists for mechanistic