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prediction to process optimization. The focus of this PhD project is to develop and apply machine learning methods across three interconnected tasks: 3D microstructure characterisation. The student will
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will design and validate advanced multi-agent Deep Reinforcement Learning (DRL) and/or Digital Twin (DT)-enabled methods for efficient, scalable and time-critical handover optimisation. The work will
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. Following an offer of employment, the successful applicant will enroll in the CBS PhD School. Further information about CBS PhD scholarships and the PhD programme can be found at https://www.cbs.dk/en
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Deep Learning: Exploring mechanistic interpretability and understanding the fundamental drivers of model performance at scale. As an early member of this fast-growing team, you will have a unique
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curation. AI Safety: Ensuring robust alignment and safety in multi-agent LLM systems Efficiency: Streamlining large-scale model experimentation and training. Science of Deep Learning: Exploring mechanistic
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Inria, the French national research institute for the digital sciences | Villers les Nancy, Lorraine | France | 17 days ago
dynamics data and advanced graph-based deep learning models to decode long-range communication pathways within macromolecular complexes. The PhD candidate will play a central role in this effort by
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multisensor fusion and ondevice AI pipelines that guarantee tight latency, power efficiency, and fail-safe robustness. Driving hardware–software codesign to radically optimize state estimation and deep-learning
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application! We are now looking for a PhD student in Computer Vision and Learning Systems at the Department of Electrical Engineering (ISY). Your work assignments Your task will be to analyse and adapt vision
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(e.g. computer vision, deep learning, AI) and green life sciences (e.g., remote sensing, crop modelling, and food security), within the European funded project AgriscienceFM (Horizon programme), which
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for structural biology. This project sits at the intersection of X-ray scattering and deep learning, aimed at integrating experimental data to predict protein ensemble structures. As an Empire AI-funded fellow