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, modelling and simulation of photonic systems, sensor systems, signal processing and device manufacturing, development of machine learning algorithms, and design of optical communication networks or power
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latency advantages over traditional multiple channel access techniques in such scenarios. As a PhD candidate, you are expected to perform the following tasks: You will conduct high quality research under
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adapt advanced machine learning frameworks (SPARKS and CEBRA) for supervised and unsupervised analysis of high-dimensional neural data to decode multisensory information Investigate how neural
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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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Robotisation (PROMAR) group, headed by Matthias Rupp. The group develops fundamental and technological expertise in machine learning for materials science, including data-driven accelerated simulations and
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spatial omics datasets. The position will also contribute to multi-modal data integration efforts that combine imaging, genomics, and machine learning approaches. Key Responsibilities Data Processing
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Previous Job Job Title Research Assistant or PhD Candidate Research Assistant Next Job Apply for Job Job ID 369568 Location Twin Cities Job Family Graduate Assistants Full/Part Time Part-Time
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one of the following analysis techniques (multiple preferred): normative modelling, dimensionality reduction techniques, machine learning, deep-learning, state space modelling, advanced statistics
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to these challenges, working with high performance and distributed computing environments, working with large-scale machine learning models, and a proven research record of scholarly contributions through publications