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equivalent. Strong background in machine learning and computer vision. Prior experience in data-efficient classification, synthesis, and detection is preferable. Strong coding skills in scientific
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the use of hierarchical graph neural networks for modeling multi-scale urban energy systems. By combining advances in Physics-Informed Machine Learning (PIML) and Graph Neural Networks (GNNs) with real
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, to produce synthetic data. Embedding models for wireless channel representation, e.g., transformer-based, leveraging reconstruction or contrastive learning paradigms. Main research field: Telecommunications
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development of secure, scalable, and reproducible machine learning (ML) pipelines and software components that support AI tool deployment in a research setting. This role requires collaboration with data
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the application of machine learning for official statistics. The subject of the thesis will be "Exploring Large Language Models for Data-to-Text Problems" and involves the study of technical methods and approaches
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pathways, including deactivation processes. Screening and fine-tuning catalysts to enhance performance. Developing workflows and machine learning algorithms to accelerate catalyst design (optional). Group
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expertise in machine learning algorithm design and applications. The research experience in the applications for radar sensing and/or communication, such as channel modelling, signal analysis & classification
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other data scientists to advance precision medicine research. Your main tasks and responsibilities: Designing and leading analyses that apply state-of-the-art generative machine learning models (e.g
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Linguistics and Psycholinguistics: Development and application of statistical machine learning, (hierarchical) Bayesian modelling, as well as advanced deep neural models for processing linguistic and visual
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mandatory core focus, the PhD position allows room for the individual research interests of the applicant to shape specific aspects—whether in modeling strategy, applied machine learning methods