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that are not seen in any other material. This project combines cutting-edge sampling techniques with machine-learned potentials for accurate phase predictions, offering considerable opportunity for method
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analysing large-scale datasets such as StatsBomb, which provide detailed technical and tactical data across multiple leagues and seasons. By applying advanced analytical and machine learning techniques
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the boundaries of bioinformatics in molecular medicine. Requirements on candidates: bioinformatics, machine learning, data science Keywords: Bioinformatics, foundational models, multiomics, single-cell
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PhD candidate in the automated detection of measurable residual disease in hematological malignancie
(deep learning, probabilistic modelling, generative AI) or machine learning Proficient in Python or R programming Strong communication skills in English Strong interpersonal skills Ability to work
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Inria, the French national research institute for the digital sciences | Rennes, Bretagne | France | 3 months ago
processing tasks, including machine learning and deep learning [4]–[6], database processing [7], [8], and networking [9]. Near-memory computing (NMC) is a memory-centric computing paradigm that has emerged as
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calculations of well-characterized 2D materials, simulations of electron microscopy images, and machine learning methods to reconstruct the 3D atomic positions of materials from a 2D microscopy image. The
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powerful framework for decentralised machine learning. FL enables multiple entities to collaboratively train a global machine learning model without sharing their private data, thus enhancing privacy
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international conferences. This can provide opportunities for networking and learning from other researchers in your field. Extracurricular Seminars and Trainings The in-house umbrella organisation INGENIUM
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on individualised data; (2) to speed up FE model computation through machine learning prediction, in order to make it usable in clinical routine; (3) to conduct experimental validation of FE prediction results, in
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combined with Machine Learning algorithms, this thesis aims to study long-term temporal trends in tropospheric ozone (O3) in Europe across different type of environments (background, rural, urban), while