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Field
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, students gain profound knowledge in key areas of infection biology. At the same time, students learn to examine their research findings critically and to present them to an audience. In the last two half
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: active learning (uncertain cases first), smart sampling, confidence thresholds, gradations (auto-label/review/manual), measurement and decision logic for throughput vs. quality. Proficiency in programming
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computer skills. We also expect successful candidates to be active team players in the RTG. Experience with data collection in an international context will be a plus. Language requirements Excellent command
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twin of sperm motility, and utilize it to develop a separation method. Your tasks will include: Performing computer simulations and matching them to experimental data Very close collaboration with
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by colloids, as well as methods for immobilizing these ions. Modern methods of theoretical chemistry (first principles, kinetic Monte Carlo, machine learning) will be applied to investigate diffusion
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streaming and batch processing. These efforts provide the foundation for advanced analytics, machine learning, and AI applications. The IDE Research School guides PhD researchers by offering a platform for
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environment of the transregio, one of the leading thoughts is the implementation of the general ''Problem-Based Learning'' principle, i.e., to combine the studies with hands on experience in research wherever
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imaging. Your Profile: The successful applicant must have the following: • Master’s degree in physics, biophysics, biomedical engineering, computer engineering or electrical engineering. • Excellent track
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), biostatistics, machine learning, data science and research data management, and causal inference methods (Iris Pigeot, Marvin Wright, Vanessa Didelez), and etiologic and molecular epidemiology (Konrad Stopsack
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missions. Prior experience with methods of statistical inference using simulations or anomaly searches with machine-learning approaches is desirable.