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Field
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the opportunity to contribute to collaborative efforts at the interface of data science, imaging, and materials research. You will strengthen the data science and machine learning activities of the IAS-9 with
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) platforms used in machine learning, big data and artificial intelligence (AI) based applications (CPUs, GPUs, AI accelerators etc.) require high power demands with optimized power distribution networks (PDNs
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machine learning frameworks like PyTorch. The candidate should demonstrate strong analytical abilities, an eagerness to learn and solve complex problems, and possess effective collaboration skills
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modelling, multi-disciplinary optimization or AI-based surrogate modelling techniques. · Soft skills: o Strong analytical and problem-solving abilities. o Excellent communication and technical
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from visual and auditory cortices recorded over multiple days Apply and adapt advanced machine learning frameworks (SPARKS and CEBRA) for supervised and unsupervised analysis of high-dimensional neural
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into the enzyme-substrate interaction in the same set of enzymes. Further, there will be collaboration with a PhD student from NTNU working with similar analytical methods on another class of carbohydrate-active
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spoken English. Strong analytical skills. Willingness to acquire a variety of additional skills ranging from physics modelling and statistical data analysis to hardware programming. PhD researchers
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We are a world class research-intensive university. We deliver teaching and learning of the highest quality. We play a leading role in economic, social and cultural development of the North East of
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analysis and analytical data analysis workflows, together with other team members Implementing AI-based microscopy image analysis software as python packages Developing algorithms to deploy machine learning
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play a central role in this interdisciplinary initiative. They will: Develop and apply machine learning (ML) methods – including surrogate modeling, feature extraction, and inverse design algorithms