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of Oxford and Valencia, on a large-scale European project. The focus of the postdoc project will be on using advanced numerical simulation modeling software and machine learning techniques to quantitatively
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your chance to make a significant impact in areas such as: Physical layer optimization Signal processing and software-defined radio prototyping Machine learning for wireless sensing What We're Looking
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current drift-diffusion modeling software, applying it to experimental data, and using machine learning to identify trends in the data. Applicants should have a PhD in physics, materials science, electrical
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the candidate’s PhD or other previous projects. These skills are essential for the development and implementation of a new machine-learning model to represent the tensorial electronic friction coefficients
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machine learning, signal processing, epidemiology and causal inference, but all candidates with knowledge at the intersection of these three scientific disciplines are invited to apply. Candidates with
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presentations at conferences) Demonstrable proactive and flexible attitude Has experience with molecular data and applying landscape modelling frameworks (e.g. SDMs) Particular strengths with Machine Learning
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with your chip(s) will be analyzed with machine-learning algorithms. You will collaborate with researchers and companies of various disciplines like chemistry, embedded systems, software, signal
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implications of such systems? We are seeking a postdoc to join our team, someone who is keen to contribute to the development of trustworthy machine learning systems and to translating algorithmic advances and
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who is keen to contribute to the development of trustworthy machine learning systems and to translating algorithmic advances and challenges in machine learning for policy makers. What are you going
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the candidate’s PhD or other previous projects. These skills are essential for the development and implementation of a new machine-learning model to represent the tensorial electronic friction coefficients