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offering PhD positions for students with a background in data science, computer science, computational science, or a domain science with a strong focus on computational science and an interest in training
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), and an increasing number of industrial partners. MUDS is offering PhD positions for students with a background in data science, computer science, computational science, or a domain science with a strong
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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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data mining. The group provides a strong network to local AI expertise (e. g. Hessian.AI, TU Darmstadt), large scale compute infrastructure, as well as a broad international network (Stanford, UC San
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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 | 9 days 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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/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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assessment of materials using microbial communities Processing and analysis of high-throughput sequence data (DNA, RNA, community data, whole genome data, data mining) Data management for the division
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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