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Field
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capabilities with a deep understanding of trading to design, validate, backtest, and implement statistical and advanced machine learning models. Your work will span a range of initiatives, including large-scale
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, an extensive training programme in respect of industry-specific skills, and access to hotfire facilities at Westcott, Machrihanish, and elsewhere. You can learn more about the programme at r2t2.org.uk. Kick
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of the PhD topic (subproject A7- Reinforcement learning for mode choice decisions): This PhD project will develop and implement a Deep Reinforcement Learning (DRL) model for dynamic mode choice within
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are using ferroelectric memories, which can calculate AI algorithms from the field of deep learning in resistive crossbar structures with extremely low power consumption and high speed. Furthermore, we
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effect can be predicted. You will acquire in-situ and remote-sensing data of cirrus forming downwind of flights over the past decade, along with measurements/estimates of local conditions and emissions
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an increasingly complex development environment. Areas to consider that impact the modelling are: Framework Language Process How wide / how deep i.e. what do we model and why? How much provides a good answer i.e
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Applicants should hold a relevant MSc degree in electronics, electrical engineering, computer engineering, or related fields. Required Qualification: Solid background in digital CMOS design and deep learning
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. For this PhD position you will work on modelling the processes and feedbacks that couple the AMOC and polar ice sheets, with particular focus on sea ice and (North Atlantic) deep-water formation regions such as
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We are offering a WASP, The Wallenberg AI, Autonomous Systems and Software Program, funded PhD position that provides a unique opportunity to develop deep expertise in robotics, machine learning
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understood how such automation solutions can be safely and robustly supported with state-of-the-art deep learning. There is a need for new AI that can incrementally learn and adapt without losing accuracy