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at the IMPRS-LM aim to dissect the spatial and temporal organization of biochemical networks in the cellular environment. The IMPRS-LM provides a unique graduate training program that equips students with
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for diversity Desirable knowledge and skills include: o Understanding of the principles that define DNA, RNA, protein structures, functions, dynamics and interactions o Experience in plant work
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the Leibniz Junior Research Group “ Probabilistic Methods for Dynamic Communication Networks“ (Head: Prof. Dr. B. Jahnel) starting as soon as possible. The position is within the Math+ project "Information Flow
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knowledge of electrochemistry and experience with fuel cell test Ability to cooperate across disciplines in an international environment Independent, structured and target oriented way of working Software
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Understanding of the principles that define protein structures, functions, dynamics and interactions o Protein structure prediction and modelling, e.g. in Rosetta, MODELLER, AlphaFold, etc. o Protein
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drive the formation of transport carriers within the endosomal network. In this mission, we primarily rely on cryo-ET and subtomogram averaging to visualize molecular machines in reconstituted form
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of neural hydrology, where hydrological models are directly learned from data via machine learning (e.g., LSTM neural networks, [1]). Initially, these models ignored all physical background knowledge and did
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selectivity mechanisms in potassium channels Simulation of electrophysiology experiments Please indicate in your application which of the above listed projects is most intriguing for you. Your profile
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research interests, relevant skills, and experience. The letter must be self-written without the use of LLMs. A Curriculum Vitae, including a full list of scientific contributions such as publications
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Generate synthetic microstructures (based on the open-source OptiMic software) Perform descriptor extraction and micromechanical simulations (MCRpy, DAMASK) Vary the material processing parameters, which