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substrates while advancing our understanding of deep learning through dynamical systems theory. You will work with two cutting-edge experimental systems: (1) light-controlled active particle ensembles
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boundaries and abstraction levels is a must. Experience with control theory and spiking neural networks are a plus. Ability for collaborative work, interdisciplinary and cross-topical thinking. Very good
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Your Job: Reinforcement Learning (RL) is a versatile and powerful tool for control, but often data-inefficient, requiring numerous updates and non-local information such as replay buffers and batch
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on cells of the innate immune system such as macrophages and neutrophils. In particular, we explore mechanisms of malignant transformation, cellular differentiation and the control of immune cell
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Max Planck Institute for Dynamics and Self-Organization, Göttingen | Gottingen, Niedersachsen | Germany | about 20 hours ago
command of English, which is the working language of the department. German is an asset but not required. Our offer We are offering excellent working conditions in a highly interdisciplinary and stimulating
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interest in one (or two) research projects as part of your application: C01 – Synthesis of organic dyes and redox systems for controlled assembly in mol2Dmat heterostructures (Prof. Eigler, 2 positions
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the application of machine learning (ML) methods or large language models (LLMs) Proficiency in Python programming and confident use of Unix/Linux environments; ideally experience with version control systems (e.g
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Description Reliable monitoring and control of water systems is essential to protect water resources, ensure hygienic standards, and enable sustainable infrastructure operation. As challenges evolve
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, chalcogenides, and semiconductor compounds, aiming to understand and control their growth. Analyze the deposited films and structures using scanning probe techniques and other complementary characterization
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With its work, the Leibniz Institute of Vegetable and Ornamental Crops (IGZ) contributes to a better understanding of plant systems and thus to the development of sustainable and resilient