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, using techniques such as: High-dimensional data mining Tensor decomposition Causal inference Statistical process modeling Machine Learning Applications include public transport, private vehicles, traffic
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transcriptomics and single-cell RNA sequencing on patient samples • Mining and analyzing public cancer databases (TCGA, GEO, etc.) and omics data • Inferring TLS formation and maturation stages from
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ontology alignment in physics and materials domains Build and maintain ontologies, OWL/RDF knowledge graphs, SPARQL endpoints, and open benchmarking suites to guarantee FAIR, reusable research data Mine and
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Leibniz-Institute for Plant Genetics and Crop Plant Research | Neu Seeland, Brandenburg | Germany | about 2 months ago
of computational load for such a development. In the frame of a third party funded research project, we are looking for a PhD Student to work on and develop breeding strategies to mine Winter-wheat accessions stored
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datasets (e.g. scRNA-seq) to derive prior knowledge on AML dormancy Develop and apply integrative data analysis pipelines (e.g. MOFA, Scriabin, LIANA+, COSMOS) for mining and interpreting multi-omic datasets
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/RDF knowledge graphs, SPARQL endpoints, and open benchmarking suites to guarantee FAIR, reusable research data Mine and link structure-property relationships from DFT, MD, phase-field, TEM/SEM, and
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for Business Information Systems (sebis) at the Technical University of Munich is looking for outstanding candidates for three PhD positions to be filled on August 1st 2025 to enlarge our research team
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sensitivity and respect for diversity Desirable: Experience in cheminformatics, molecular modeling, or enzyme mechanisms Familiarity with databases, data mining, and knowledge management Why Join Us: Be part of
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third party funded research project, we are looking for a PhD Student to work on and develop breeding strategies to mine Winter-wheat accessions stored in the IPK genebank: Your tasks You work with elite
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, the concept of a cross-university PhD programme involving non-university institutions was developed to enable the combination of different scientific and technological disciplines relevant to biomedical data