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scalable training and inference on parallel, distributed and GPU-accelerated computing systems Benchmark the developed approaches against established methods, assessing predictive performance, generalisation
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benchmark them with a realistic case study. The main focus of the project can develop either more in the mathematical theory of MCMC, the implementation of code for the Jülich supercomputers (GPU/CPU
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, microfluidics, laboratory automation, and GPU computing infrastructure. The opportunity to develop AI methods and scientific software that are directly deployed on cutting-edge experimental platforms. Vacation
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, optimization, or high-performance computing is highly desirable Experience with quantum software frameworks (e.g., Qiskit, PennyLane, Cirq) or HPC programming (MPI, OpenMP, CUDA, GPU computing) is considered
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at the interface of AI, imaging, microbiome research, and laboratory automation within a Helmholtz-wide collaboration. Access to state-of-the-art live-cell imaging, microfluidics, laboratory automation, and GPU