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Inria, the French national research institute for the digital sciences | Montbonnot Saint Martin, Rhone Alpes | France | 2 months ago
for representing knowledge graphs and OWL for formalising ontologies. In order to manage the heterogeneity of knowledge, alignments between ontologies make it possible to express the relationships between concepts
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Leibniz-Institut für Analytische Wissenschaften – ISAS – e.V. | Dortmund, Nordrhein Westfalen | Germany | about 2 months ago
for the Research Group Multidimensional Omics Data Analysis: PhD Candidate (m/f/d) You will be responsible for Setting up a knowledge graph in neo4J for microbiome research Integration of microbiome research data
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for analyzing how landscapes evolve over time. This thesis is part of the ANR GEvoK (Geographic Entities Evolution in Knowledge Graphs) project, which aims at automatically detecting and semantically
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, and EHR data. Experience with modern deep learning frameworks such as PyTorch, TensorFlow, Keras, Scikit-learn, Pandas, NumPy, and SciPy. Familiarity with convolutional neural networks (CNNs), graph
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control with Git, typesetting with LaTeX, use of Linux computers; Experience with convolution and transformer-based neural networks for image analysis; Experience with graph-based methods, and graph
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(Kirchhoff's laws) as soft or hard constraints, operational bounds (voltage limits, capacity, phase balance), and network topology through graph neural network architectures. You will build validated benchmark
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; Build data and knowledge infrastructures, namely knowledge graphs, ontologies and multimodal representations of the CENSE scientific body; Collaborate with the CENSE team and external partners in
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qualification. Professional assignment: Chair of Scalable Software Architectures for Data Analytics (Prof. Dr. Michael Färber) Research areas: Natural Language Processing, Large Language Models, Knowledge Graphs
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Campus (LLEC). Development of physics-informed and graph-based machine learning methods for energy system monitoring, forecasting, and planning Data analysis considering uncertainties, missing data
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learning pipelines for multilayer segmentation and nanoscale transistor classification from microscopy images; (b) Designing graph-based inference models capable of reconstructing higher-level logical blocks