MASTER THESIS: ON-CHIP SPIKING ROBOT CONTROL BASED ON REINFORCEMENT LEARNING (M/F/D)
In the Neuromorphic Computing department, we search for a talented
Master Student Neuromorphic AI Vision (m/f/d) (non paid)
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Perform research on Spiking Reinforcement Learning with an event-based camera on a KUKA IIWA robot (simulated and hardware)
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Setup an experiment where the robot learns to insert an object in a hole based on visual guidance from the event-based camera
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In the frame of hip manufacturer, become part of a team that works in event-based machine vision for robotics applications
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Implement your research on the Loihi neuromorphic chip and state-of-the-art event-based cameras
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Be an active part of the Intel Neuromorphic Research Community and participate in workshops, present your work, write publications
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Simulate on the Neurorobotics Platform (our neuromorphic computing simulation tool), and connect with a large European team of experts and the EU flagship Human Brain Project
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Your profile:
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Enrolled in a Masters Program in artificial intelligence, machine learning, robotics, neuromorphic computing or a similar topic
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Strong hands-on skills in Deep Learning, especially in image and/or sensor processing
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Ability to read and implement machine learning related math
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Fluency in software development in Python and/or Javascript, C++
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A passion for applied science, technology and product development
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Strong relational skills, positive thinking and team player mindset
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Our offer:
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Immersion in an ecosystem of world leading research labs in neuromorphic computing within the INRC
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Participation in a prestigious European project of global prominence in simulation-based neuroscience (Human Brain Project)
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First class offices in central Munich (Nordfriedhof) in the 27th floor with panoramic view on the city and the alps
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Friendly atmosphere, agile work flows
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Did we catch your interest?
Please submit your application with a motivational statement, a detailed CV and a current transcript of records to our job portal. https://recruitment.fortiss.org/
Job-ID: NC_MS_01-2021
Contact: Axel von Arnim