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, since January 1, 2026, as part of the Excellence Strategy of the German Federal and State Governments, BioSysteM is administered by TUM. The aim of BioSysteM to learn how to design and control biological
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. The position The position is available within a multidisciplinary effort to develop new machine learning-based techniques for Natural Products Genome Mining at the Cluster of Excellence Controlling Microbes
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Max Planck Institute for Intelligent Systems, Tübingen, Tübingen | Bingen am Rhein, Rheinland Pfalz | Germany | about 2 months ago
for use in research, film, virtual reality, biology and medicine. Using unique 3D & 4D capture facilities, machine learning, computer vision and advanced graphics, we are modeling every nuance of how humans
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of laser technology—is offering the opportunity to write a thesis on the topic: »AI-based local quality prediction for laser powder bed fusion«. We are currently developing machine learning-based approaches
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research should apply modern methods of robotics, such as stochastic and data-driven learning approaches, precise monitoring and control using microcontrollers (μC), or novel methods of image processing. In
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Max Planck Institute for Heart and Lung Research, Bad Nauheim | Bad Nauheim, Hessen | Germany | 2 months ago
(BTA, MTA, or equivalent) Excellent communication skills in both German and English Ability to work independently as well as collaboratively within a dynamic team Strong motivation to learn new methods
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project, modern machine learning (ML) methods will be developed, trained, and tested based on these real-world datasets—ideally integrated directly into ongoing plant operation. A key application example is
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optical communication networks and systems, as well as machine learning, computer vision and compressing digital videos.Become a part of our team and join us on our journey of research and innovation! Be
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approaches include data-driven parameter estimation for white-box models, the development of black-box models using machine learning methods (e.g., neural networks) and their combination (grey-box). The thesis
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for digital patient twins in oncology and cardiovascular medicine. These models should learn patient representations across data types, organs, diseases, and time, capturing disease trajectory, prior history