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theoretical challenges motivated by astrodynamics, optimisation, control, scientific machine learning, mission design and autonomous systems, translating these into new research directions in deep learning
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for an enthusiastic and motivated person with good communication skills to join our research team. The PhD candidate should have: Master’s degree (or comparable) in Machine Learning, Data Science, Computer
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has a fixed-term opportunity for a Postdoctoral Fellow / Senior Research Fellow to contribute to world-leading research in continual learning, computer vision, multimodal foundation models, and
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 1 month ago
desirable. Candidates with experience in time-series machine learning, advanced data analytics, and programming (Python) are especially encouraged to apply. Special Physical/Mental Requirements Special
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Computing, Cryptography, Satellite Systems, Vehicular Networks, and ICT Services & Applications. Your role The PhD student will contribute to a large industrial research collaboration with a major
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turnaround through autonomous planning and execution Seamless transition from prototype to production-deployed AI systems Basic Qualifications: PhD (or MS + 3 yrs / BS + 5 yrs equivalent experience) in Machine
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submitted a relevant PhD and have expertise in behavioural science, AI, machine learning and/or an intersection of those fields. This expertise should be explained in a letter of application and demonstrated
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of small group teaching – for example problem and teams based learning, tutorials. Literature review and academic poster supervision and assessment. Supervision and assessment of undergraduate research
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at NTU are looking for a Research Fellow (RF) to carry out research in probabilistic machine learning and GenAI, by exploring cutting-edge approaches such as sequence model design, continual learning
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causal analytical foundations of mass transit system performance. The research is expected to contribute both novel methodological advances in statistical modelling, causal inference, and machine learning