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the use of modern machine‑learning methods within applied mathematics—particularly physics‑informed learning, anomaly detection, data‑driven modelling, and the construction of surrogate models grounded in
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blood samples to advance patient care. This role will involve developing computational models (statistical, machine learning, etc.), and using them to perform high throughput analysis of clinical data
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with a focus in economics, or related disciplines strong analytical and methodological skills with a focus on quantitative data analysis (e.g., econometrics, statistics, machine learning) a high
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the knowledge acquired during the PhD with team members and acquire new knowledge. - Engage with the Local team at LIPN and the wider national community working on proof theory, programming languages and
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to groom the next generation of leaders, thinkers, and innovators to thrive in the digital age. Located in the heart of Asia, NTU’s College of Computing and Data Science is an ‘exciting place to learn and
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. The projects focus on learning from bats with exceptional healthy longevity to develop bat-inspired new targets and strategies to fight human diseases and aging, and have multiple national and international
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. Possessing a Master’s or PhD degree will definitely be advantageous. Knowledge of machine learning, pytorch, huggingface etc... Knowledge of speech, audio, time-series or signal processing is required. Ability
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machine learning methods for detecting, classifying, and identifying wireless anomalies in real-world radio environments. You will design and experiment with AI-driven approaches for spectrum analysis, work
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Inria, the French national research institute for the digital sciences | Villeneuve la Garenne, le de France | France | 10 days ago
/Qualifications Technical skills and level required : mathematics, programming Languages : English Relational skills : good communication skills Specific Requirements The candidate must hold a PhD in machine
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fellow devotes most of their time to research. There is the possibility of teaching up to 20%. Requirements PhD degree in machine learning, automatic control, system identification, signal processing