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district heating networks, within the framework of the project "Data Analysis for Peak Load Stabilisation in District Heating Networks (DAS)". The work includes: design and implementation of RL algorithms
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-from-motion, and object recognition. The main research problems include mathematical theory, algorithms, and machine learning (deep learning) for inverse problems in artificial intelligence, as
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Networks (DAS)". The work includes: design and implementation of RL algorithms to address the challenges of peak load variations in district heating systems development and use of simulation models
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to have good knowledge of computer science, mathematics, algorithms, and programming. Knowledge and experience in artificial intelligence and machine learning is expected, but not required. Knowledge and
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. You have a strong ability to solve problems and formulate algorithms. You also have an interest in genetics and genomics. You are organized, proactive, and can work independently as
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the research and education has a unique breadth, with large activities in classical scientific computing areas such as mathematical modeling, development and analysis of algorithms, scientific software
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, use imitation learning algorithms to learn pick-and-place actions, design HRI experiments with users, evaluate data, and share the code and benchmarks in open repositories. This postdoctoral position is
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the real world based on a seamless combination of data, mathematical models, and algorithms. Our research integrates expertise from machine learning, optimization, control theory, and applied mathematics
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develop new algorithms where needed: this may include the incorporation of genomic or other omic data 2) An important second part of the post is helping to automate components of interpretation and
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data for urban characterization. The work includes developing algorithms, performing large-scale analyses, and collaborating with partners across disciplines in remote sensing, urban studies, and climate