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manipulation in microfluidic environments Design and implement reinforcement learning algorithms for control and manipulation, first in simulation and later on real experimental setups Refine a real-time
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physics-aware simulations of growing cell populations, including their spatiotemporal manipulation in microfluidic environments Design and implement reinforcement learning algorithms for control and
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interest in at least one of the following areas: System dynamics and control Biomechanics and musculoskeletal modeling MATLAB/Simulink, OpenSim, or similar modeling tools Robotics, mechatronics
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advanced degrees in biomedicine, bioinformatics, computational biology, or related fields to gain hands-on experience in a dynamic research environment. Position Status Part Time Posting Number 24FA0702