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of everyday life. This project aims to change that by developing AI-driven methods to assess wellbeing through video-based sentiment analyses. As a PhD student, you will develop and refine machine learning
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23 Dec 2025 Job Information Organisation/Company University of Siegen Research Field Computer science » Computer architecture Researcher Profile First Stage Researcher (R1) Positions PhD Positions
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, dynamic academic setting. Eligibility criteria Recently obtained PhD (no earlier than 2024), in one of the following fields : Bioinformatics Computational biology Computer science / data science Applied
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. This PhD will focus on uncertainty-aware machine learning models, developing and evaluating techniques (e.g., Bayesian and interval neural networks) to quantify model uncertainty and monitor it during
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Your Job: We are looking for a PhD student to contribute to the development of fast, accurate, and physics-informed machine learning models for predicting blood flow in patient-specific vascular
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8 Dec 2025 Job Information Organisation/Company Université Catholique de Louvain (UCL) Department ICTEAM Research Field Computer science » Computer systems Researcher Profile Recognised Researcher
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The International Institute of Molecular Mechanisms and Machines Polish Academy of Sciences | Poland | 8 days ago
of PhD Student in Warsaw, Poland, for the implementation of the project OPUS 29 financed by the National Science Center. Group Leader: Prof. Agnieszka Chacinska (https://imol.institute/leaders/chacinska
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and/or the CAD/CAM process is a plus. I am proficient in Python and am familiar with data science and machine/deep learning toolkits. As a PhD researcher at KU Leuven, I perform research in a structured
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/bmi.tvoed.entgelt Further information on doctoral degrees at Forschungszentrum Jülich (including its various branch offices) is available at https://www.fz-juelich.de/en/careers/phd Further information on the project
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SD- 26053 PHD IN ULTRA-FAST MACHINE-LEARNING INTERATOMIC POTENTIALS FOR NANOINDENTATION OF TIC MA...
PhD candidate to develop and apply ultra-fast machine-learning interatomic potentials (UFPs, Xie et al., npj Comput. Mater., 2023, 10.1038/s41524-023-01092-7 ) for long, multi-million-atom molecular