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new theoretical approaches for understanding stability, generalization, and feature learning in large-scale neural networks. Further details and application instructions are available at: https
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5th November 2026 Languages English English English We're seeking a PhD candidate in Biotechnology to tackle antimicrobial resistance. PhD in Bioinformatics and Machine Learning for Infection and
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power systems, network analysis and power flow; - Experience or academic background in machine learning, Graph Neural Networks (GNN)/Grid Foundation Models and/or probabilistic methods and Monte Carlo
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spectroscopy, LIBS and/or XRF, together with calibration, multimodal co-registration, data fusion and machine-learning methods.; The research will involve several main tasks:; • Underwater Sensor Development and
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for probabilistic unsupervised learning for structured biological data. For more information and how to apply: https://www.jobbnorge.no/en/available-jobs/job/307053/3-years-phd-position-in-probabilistic-machine
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Inria, the French national research institute for the digital sciences | Montbonnot Saint Martin, Rhone Alpes | France | 10 days ago
and Objective: Tabular and Time-series foundational models have become very popular due to their high accuracy relying solely on In-Context Learning (ICL) [3, 4]. However, one still requires training
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of ORCRIST: Optimized Ray-tracing for Cloud-Radiation Interaction Simulations in 3D using GPUs and Machine Learning, funded by the NWO Open Technology Programme. Sunlight and heat radiation move
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de Barcelona. https://www.ub.edu/escoladoctorat/en/access-and-admission/access-pathwa… ; Specific Requirements Experience in global optimization and machine learning interatomic potentials would be
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-temporal-resolution bulk measurements (XAS, Raman spectroscopy, XRD), with particular emphasis on pair distribution function (PDF) analysis. Machine learning approaches will be used to support these analyses
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, the United Kingdom, Germany, Belgium, Ireland, Spain, Norway, and Switzerland. They range from fully experimental to fully computational: Discovery and annotation (DC1 to DC5): machine learning for cross