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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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within the broad topics of modelling tool-workpiece interaction in mechanical material removal processes, zero-defect manufacturing, machining system performance characterization as well as on-machine and
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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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will exploit multidisciplinary consortium expertise spanning design, modelling and simulation of photonic systems, sensor systems, signal processing and device manufacturing, development of machine
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with—or documented interest in—advanced statistical methods for causal inference in observational data, model-based imputation, genetic epidemiology, or the application of machine learning to registry
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and image analysis (MATLAB or Python), machine learning techniques, and basic programming/coding will be a plus. Fluency in English is mandatory. Willingness to work in an inter-cultural and
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+to+apply#Howtoapply-Eligibility) a Master’s degree in Artificial Intelligence, Machine Learning, Computer Science, Cognitive Science, Psychology or a related field excellent knowledge in AI and at least one
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, electrical engineering, technical medicine, or a related field. You have a solid background in biomedical signal analysis, physiology dynamic system, and machine learning technologies, and preferably have
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Grant, focusing on the development of novel deep learning tools to recommend reaction conditions for the synthesis of novel TRPA1 inhibitors. The project “A machine learning approach to computer assisted
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proficiency in relevant programming languages (e.g., Python, C++) and tools such as ROS. Experience in simulation and digital twins, as well as the use of synthetic data for training machine learning models, is