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) or Machine Learning models. These tools will be integrated with physics-based models of environmental loading (waves and wind) to enhance the accuracy and robustness of the assessment. All components assembled
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(e.g., Kalman Filter) or Machine Learning models. These tools will be integrated with physics-based models of environmental loading (waves and wind) to enhance the accuracy and robustness
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or development of machine learning methods, or a desire to learn these skills, are also welcome. We offer the opportunity to work on interesting scientific challenges using modern experimental methods available in
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bio materials and porous materials PhD student candidate 2 with background in computer science, AI, machine learning or related fields with the experience in CFD, ANSYS, COMSOL The successful candidates
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computationally efficient numerical structural models. To support the condition (state) assessment, the project will also explore the use of advanced estimators (e.g., Kalman Filter) or Machine Learning models
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at Aalto University (https://into.aalto.fi/display/endoctoralsci/How+to+apply#Howtoapply-Eli… ) a Master’s degree in Artificial Intelligence, Machine Learning, Computer Science, Cognitive Science
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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