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Field
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to make viable trading decisions under high price volatility. This PhD position focuses on designing, developing, and evaluating self-learning energy trading algorithms that are able to cope with
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modern technologies is a highly active topic at the interface of fundamental research and applications. The development of quantum technologies has been identified as a national strategic area. Furthermore
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systems, mainly plants. The BM^2 Lab is mainly computational and uses ad-hoc developed modeling tools such as MorphoMechanX to provide explanatory and predictive scenarios for developmental problems. We
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-body positron emission tomography (PET) with magnetic resonance imaging (MRI). In conjunction with the development of algorithms, dedicated software and hardware-based simulations will be developed
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the entire system, where many interconnected modules affect each other. In this project, you will be designing algorithms to guarantee the reliable operation of semiconductor machines, together with a highly
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this PhD project, you will investigate the co-design between event-based learning algorithms and neuronal hardware units with multi-scale time constants. The algorithmic methodology will exploit recent
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GNSS alone leaves systems vulnerable to interference, spoofing, or outages, particularly in dense urban environments. The development of 6G networks with integrated TN and NTN infrastructures provides
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control and reinforcement learning supported by an edge-cloud-based wireless communication environment. The doctoral student will work on data-driven theory and method development in simulation environments
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regulation, RNA biology, protein biochemistry, stem cells, bioinformatics, sequence analysis, mathematics, statistics, molecular evolution, or biophysics, and wish to work at the Max Planck Institute
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creation of a database for the various pollution sensors with a view to training online (non-embedded) models in the first instance. - Development of a machine learning algorithm based on the study database