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the project include mathematical derivation, analysis, and comparison of models, methods, and simulation approaches; rapid prototyping of new ideas in custom code; implementation of new models, methods, and
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methods (LBM). For fluid simulations, we utilize the high-performance LBM framework waLBerla, predominantly written in C++, but increasingly adapted for GPU computations through automatic code generation
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., for convergence, existence, and uniqueness of solutions Fast prototyping of new ideas in individual code Implementation of new models, methods, and algorithms into an existing framework, with a focus on efficiency
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• Integrated sensing and communication: fundamental limits and algorithm design (1 PhD, Mari Kobayashi, mari.kobayashi@tum.de) • Optical fiber channel modeling, receiver processing, and coding (1PhD, Gerhard
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++ coding skills • Experience with deep learning frameworks (e.g., TensorFlow, PyTorch) • Enthusiasm and self-drive towards driving research forward :) How to Apply: • Required Documents: CV, research
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++ coding skills • Experience with deep learning frameworks (e.g., TensorFlow, PyTorch) • Enthusiasm and self-drive towards driving research forward :) How to Apply: • Required Documents: CV, research
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++ coding skills • Experience with deep learning frameworks (e.g., TensorFlow, PyTorch) • Enthusiasm and self-drive towards driving research forward :) How to Apply: • Required Documents: CV, research
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- Proficient C++ coding skills (this is critical and will be tested) - Experience with deep learning frameworks (TensorFlow / PyTorch) - Excitement, self-motivation, and commitment to revolutionize the field