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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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are researching this and similar questions. Our goal is to systematically optimise the interaction between people, organisations and technology. With us, your theory also passes the practical test. Bring your
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the fields of medicine, biology, chemistry, physics, technology, and the humanities. Since 1948, MPI researchers have been awarded 21 Nobel prizes, which testifies to the quality and innovation of MPI research