Postdoctoral fellowship in Learning and Control of Avian-Inspired Drones
The EPFL Laboratory of Intelligent Systems invites applications for a postdoctoral fellowship to study learning and control of agile flight in avian-inspired drones. The candidate will join a team of postdocs and PhD students working on mechanical design, aerodynamic modelling, meta-materials, embedded computation, machine learning, and control of new feathered drones with unprecedented agility and multi-modal locomotion. The laboratory has access to supercomputing facilities, to a motion-tracking hall with an embedded wind tunnel for testing and characterisation, and to dedicated workshops. The project is carried out in collaboration with the laboratory of Prof. Davide Scaramuzza at University of Zurich. We are particularly interested in candidates with experience in learning and/or biologically inspired control and a strong motivation to make foundational scientific contributions.
Background reading:
Ajanic, Feroshkan, Mintchev, Floreano (2020). Bioinspired wing and tail morphing extends drone flight capabilities, Science Robotics, 5(47), DOI: 10.1126/scirobotics.abc2897
Applicants should send an email to [email protected] with a CV, names and contacts of two references, and a full publication list. Selected applicants will be contacted for interviews. The position is available immediately and will remain open until a suitable candidate is found.
Postdoctoral fellowship in Learning and Control of Soft Grasping
The EPFL Laboratory of Intelligent Systems invites applications for a postdoctoral fellowship in learning and control of soft grippers. The postdoctoral fellow will join a diverse team of postdocs and PhD students working on mechanical design, materials, and integration of compliant and lightweight grippers for grasping, perching, and manipulation of rigid and deformable objects. The project is carried out in collaboration with the laboratory of Prof. Herb Shea. Areas of interest include, but are not limited to deep neural learning, evolutionary algorithms, computer vision, and biologically inspired control.
Background reading:
Shintake, Rosset, Schubert, Floreano, Shea (2016). Versatile soft grippers with intrinsic electroadhesion based on multifunctional polymer actuators, Advanced Materials, 28(2), DOI: 10.1002/adma.201504264
Applicants should send an email to [email protected] with a CV, names and contacts of two references, and a full publication list. Selected applicants will be contacted for interviews. The position is available immediately and will remain open until a suitable candidate is found.