40 parallel-processing-bioinformatics "HZI" PhD positions at Forschungszentrum Jülich
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Your Job: Possible doctoral research topics: Designing processes and plants for the synthesis and use of chemical hydrogen storage Integration of chemical hydrogen storage systems into industrial
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Your Job: As part of your doctoral project, you will work on the production of the energy carrier dimethyl carbonate (DMC) using an innovative, sustainable, and catalytic process that exploits
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Your Job: Conducting in-situ and in-operando Raman experiments on glass corrosion Developing and constructing fluid cells with integrated alpha radiation sources Performing post-mortem analyses (electron microscopy e.g. EPMA, SEM, and X-ray methods e.g. µCT, XRD) Preparing and evaluating...
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communities and interfaces to higher-level grids (electricity) for stable operation and effective market participation Supporting case studies with municipal and regional partners to bridge research and real
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Your Job: Healthy brain function relies on dynamic changes at the synapse. The relevant synaptic turnover and plasticity processes span spatial scales from the molecular up to the network level, and
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environmental geophysics. This PhD project aims to advance the process-based understanding of SSF by combining state-of-the-art geophysical methods with controlled field experiments and numerical modeling
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underlying these processes remain poorly represented. This PhD project aims to fill that gap by using molecular simulations to quantify the interactions of SOM and EPS with water and minerals, and to connect
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GPU-capable, parallelized simulation frameworks. Work closely with experts in HPC and power systems to enhance scalability and computational performance. Disseminate your findings through scientific
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engineering Very strong mathematical and algorithmic background Programming experience (Python, C++, etc.) Familiarity with parallel programming frameworks (e.g. MPI, CUDA) Fluent in written and spoken English
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Your Job: Modeling and characterization at molecular level of selected biological processes by performing classical molecular dynamics, and employing enhanced sampling methods and machine learning