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Field
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. Specifically, the PhD candidate is expected to contribute corpora preparation (collection and organizing the annotation), use machine learning approaches for irony detection, and testing for experimental and
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, evaluating, and fine-tuning machine learning models (e.g. deep neural networks) to segment underwater scenes and classify anomalies. The work will explore the use of virtual environments and synthetic datasets
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modeling of dynamic systems. Experience with machine learning or AI methods applied to robotics (e.g. reinforcement learning for control, or data-driven modeling) is a plus, especially if applied in
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consist of the following: Mathematical analysis of ecological and eco-evolutionary models, involving pencil-and-paper calculations; Computer simulations of more complex models which do not easily lend
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and documented background in machine learning, deep learning, data analysis and programming. Previous experience in research and knowledge in bioinformatics, biophysics, biochemistry, molecular biology
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– from the modeling of material behavior to the development of the material to the finished component. PhD Position in Machine Learning and Computer Simulation Reference code: 50145735_2 – 2025/WD 1
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need expert knowledge in bioinformatic data analysis. Strong expertise in multi-omics data analysis (using R and Python) and a deep understanding of machine-learning models are must-criteria
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, data science, computer science, and computer engineering, including artificial intelligence (AI), machine learning, internet of things (IoT), chip design, cybersecurity, human-computer interaction
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datasets, therefore, there will be a focus in the implementation of models for large volumes of data. The project will work in an exciting interface of statistics and machine learning and has the potential
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criteria Demonstrated knowledge and experience in electrical system modeling and analysis, applied control, power electronic systems, optimization techniques, and/or machine learning. Experience with