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. This role will contribute the development of novel machine learning algorithms for wireless sensing and communication and the proof of concepts for next-generation wireless communication, collaborating with
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implementing innovative signal processing algorithms for radar sensing and communication, establishing clear research objectives, and engaging in the collaborative projects. In addition, the position involves
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into: Developing and implementing Quantum Dynamics algorithms for non-adiabatic. Studying the photo-activated non-adiabatic photochemistry of prototypical chromophores. Simulating measurable signals from time
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, harmfulness, jailbreaking, and power-seeking behaviours. You will work alongside Dr Nicola Paoletti and a core team that includes a PhD student from Dr Paoletti’s lab, Prof Osvaldo Simeone from the Department
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, harmfulness, jailbreaking, and power-seeking behaviours. You will work alongside Dr Nicola Paoletti and a core team that includes a PhD student from Dr Paoletti’s lab, Prof Osvaldo Simeone from the Department
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biomarkers, designing algorithms that capture temporal change, and incorporating clinician-informed explanations to build trust and transparency into predictions. Working closely with experts in ophthalmology
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. The project seeks to develop mathematical models for allocation of critical resources during pandemics. Populating these instances with real-world data we would then develop novel algorithms to solve them
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The ideal candidate will have a PhD in a relevant subject such as statistics, mathematics, physics, operations research, computer science or data science, and have a strong statistics background
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have: A PhD (awarded or near completion) in computational biology, bioinformatics, ecology, statistics, applied mathematics, physics or a related field. Strong programming skills in Python, R, C/C
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during pandemics. Populating these instances with real-world data we would then develop novel algorithms to solve them. The selected candidate would disseminate their research by publishing in top-tiered