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to unfused enzymes. The successful candidate will Develop coarse-grained (CG) models for CAR and other enzymes of the cascade based on atomistic simulations provided by the project team. Run Brownian dynamics
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meetings, conferences, and as scientific papers Contribute to educational events, such as university lectures, JSC courses and hackathons Your Profile: Excellent masters degree and subsequent Ph.D. degree in
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to different domains. Specifically, you will: Develop, implement, and refine Machine Learning (ML) techniques for self-supervised Deep Learning (DL) for scientific and large-scale datasets Implement parallel ML
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motivated team at Forschungszentrum Jülich, which is one of the largest and best-equipped research facilities in Europe You will have the opportunity to participate in project meetings and education events
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Jülich, which is one of the largest and best-equipped research facilities in Europe You will have the opportunity to participate in project meetings and education events organized in the frame