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. The successful candidate will work at the intersection of multi-disciplinary modelling, advanced AI algorithms, and decision-support tool development. Responsibilities will include programming, analysing and
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this project, we aim to: Develop real-time ultrasound algorithms to estimate fascicle length in antagonistic leg muscles (tibialis anterior and soleus) in healthy individuals during walking. Translate and
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of teaching and research, the FSTM seeks to generate and disseminate knowledge and train new generations of responsible citizens in order to better understand, explain and advance society and environment we
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Digital twin railway network School of Mechanical, Aerospace and Civil Engineering PhD Research Project Self Funded Prof D Fletcher, Prof R Harrison Application Deadline: Applications accepted all
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Max Planck Institute for the Structure and Dynamics of Matter, Hamburg | Hamburg, Hamburg | Germany | 17 days ago
Experience in HPC computation (application and algorithm/code development) Willingness to closely collaborate with experimentalists and theoretician. Joint research approach of all ERC synergy team members
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an age of digitalisation’ (Principal Investigator, Prof. Tamar Sharon), an interdisciplinary team science project funded by an Ammodo Science Award, investigating ethical, legal and societal aspects
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Bay. The key responsibilities of this role include; Using a combination of automated algorithms and manual data processing to identify bottlenose dolphin signature whistles in a multi-year acoustic
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energy; thereby minimising farming’s environmental impact. AI machine learning offers a new expedient method of developing control systems for tasks that would be difficult to manage using classical
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, spinal cord injury etc) through real-time neural control of wearable robotic exoskeletons. You will be developing next-generation (low and high-level) control algorithms for wearable exoskeletons that use
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, at the University of Cambridge, UK. The Research Assistant will work together with a team of students and research collaborators on the development of learning-based control policies that facilitate