88 molecular-modeling-or-molecular-dynamic-simulation "Multiple" PhD positions at DAAD in Germany
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Description Project background: Molecular spin qubits are an appealing platform for quantum information processing due to their potential for long coherence times and high chemical tunability
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mechanics (QM / MM) molecular dynamics simulations using a Quantum Centric Supercomputing (QSC) approach [1,2] for the QM problem. This will be integrated within the in-house MiMiC framework [3] and applied
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profound knowledge in computational and theoretical physics/chemistry. Capability of team work is essential. Skills in high-performance computing, materials chemistry, theoretical chemistry, molecular
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Description The MAPK pathway plays crucial roles in cancer cell proliferation, survival, and therapy resistance. This project aims to dissect the compartment-specific regulation of ERK1/2 signaling, understand its role in therapy resistance, and identify novel regulators using innovative CRISPR...
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Description The Molecular Nanophotonics Group at the Faculty of Physics and Earth System Sciences at Leipzig University is currently offering a doctoral researcher position under the supervision
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coating, iii) investigation of system design from small-scale to potentially pilot scale, and iv) application to micropollutant removal. Modelling aspects are open to exploration at molecular and process
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features allow overcoming such limitation? The PhD project will be largely experimental with some modelling aspects, and will begin with an identification of a set of research questions based on a detailed
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Description The Institute of Systems Biotechnology (Director: Prof. Dr. Christoph Wittmann) stands for excellent research at the interface of biotechnology, systems biology, and sustainable resource utilization. We conduct research in the field of sustainable bioeconomy, focusing on the...
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– a journey to become an independent researcher! Throughout the project, there will be multiple opportunities for cooperation with internal and external partners, supervision of Master students, giving
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dynamics inherent in hydrological systems, offering a promising direction for integrating machine learning with physical modeling. The potential of neural ODE models in hydrology has been discussed in [3