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Field
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, applied mathematics, or computer science Programming skills, ideally in Python, C++, or Fortran Knowledge of topics like fluid-structure interactions or monolithic/partitioned couplings or numerical methods
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(or equivalent) in Computer Science, Engineering, Robotics, or a related field Excellent programming skills in C++ and Python Strong motivation to conduct innovative research at the intersection of machine
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partners. The postdoctoral researcher will also contribute to teaching in areas such as Machine Learning, NLP, AI for Education, Explainable AI, and Python-based applied seminars, supporting course
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High Performance Computing on CPUs and GPUs experience with simulations on supercomputers producing large volumes of data proficiency in Fortran, Python, shell scripting, and processing simulation data
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, informatics, physics or a related field strong expertise in machine learning strong interest in high performance computing on CPUs and GPUs proficiency in Fortran, Python, shell scripting proficiency with Linux
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important - Prior experience with density functional theory or machine learning is desirable - Proficiency in the Python programming language is important, as well as Fortran - Strong written and oral
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the solution by integrating state-of-the-art MARL methods, with a strong emphasis on scalability and reliability. What you contribute Experience with Python ML stack, e.g. TensorFlow, Stable-Baselines3 Strong
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Previous experience in protein biochemistry, LC/MS based mass spectrometry Previous experience in handling omics data analysis using software Experience in R and python is a plus but not mandatory Good
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in transcriptomics, epigenomics, and/or genomics Strong analytical skills, as well as broad experience with scripting languages (e.g., Python, R, Bash); knowledge of common bioinformatics software and
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via Ethernet as well as data processing and data synchronization within linked systems. Know-how in EMC-compliant assembly of electrical systems would be a plus. Programming skills with Python and C/C