26 distributed-algorithm-"Meta"-"Meta" positions at Forschungszentrum Jülich in Germany
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Your Job: As part of an interdisciplinary project team with researchers from bioinformatics you will work on quantum algorithms for drug discovery. Here, the focus lies on machine learning and
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, enabling algorithm–circuit co-optimization across the computing pipeline with respect to key metrics such as power consumption, computational delay, and area efficiency. Beyond circuit prototyping
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Your Job: Investigate current challenges and bottlenecks in power flow analysis for large scale electrical distribution grids Apply machine learning/AI or surrogate modeling (e.g., neural networks
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Your Job: Energy systems engineering heavily relies on efficient numerical algorithms. In this HDS-LEE project, we will use machine learning (ML) along with data from previously solved problem
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an innovative reconstruction algorithm and methods to correct factors impacting the quantitative accuracy of PET images. Our recent research has focused on dual-tracer PET imaging, in which two radiotracers
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are as important as high accuracy. Supervise student projects at BSc, MSc, and PhD level. Work with experts at the Jülich Supercomputing Centre (JSC) to run your algorithms/tools on large distributed
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experimental settings. In addition to fieldwork, the PhD candidate will contribute to the development of novel inversion algorithms for EMI and GPR based on full-waveform inversion techniques. These methods aim
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. Because of the specific structure of the inference problems occurring in metabolic models, direct application of these MCMC algorithms is, however, not possible. In this project, you will bring MCMC methods
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mechanisms, optimisation algorithms and renewable energy systems WORK-LIFE BALANCE: Optimal conditions for balancing work and private life, as well as a family-friendly company policy. The option for flexible
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, optimisation algorithms and renewable energy systems WORK-LIFE BALANCE: Optimal conditions for balancing work and private life, as well as a family-friendly company policy. The option for flexible working