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science, embeddings and software agents is welcomed. The EBU-NEO tech-stack consists of: Python, LangChain, LangGraph, MongoDB, Milvus, Elastic Search. Knowledge from the fields of media science, user
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learning, data science, atmospheric sciences, geophysics, or related fields. Solid numerical modelling and programming skills (e.g., Python, TensorFlow, scikit learn) are essential, along with a basic
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Experience with scientific programming (e.g. in python) Experience with modelling of disordered materials is seen as an advantage Experience with automated computational workflows is seen as an advantage
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programming, e.g., in C++, Python or Matlab. Who we are The successful candidate will be hosted by the Section on AI & Sound. This section is led by Prof. Jan Østergaard. A dedicated supervisory team composed
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communication. Strong competences in mathematical modelling and simulation of flow, heat transfer and dynamic system behaviour, including use of tools such as MATLAB/Python and CFD. Strong hands-on experience
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, including signal processing, optimization, machine learning or information theory; Experience in programming, e.g., in C++, Python or Matlab. Qualification requirements PhD stipends are allocated
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communications, fundamental knowledge on computing architectures, and advanced programming skills (e.g., Python, C++, Matlab/Octave). Experience with simulation, emulation, and modelling tools for signal
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materials chemistry and excellent analytical skills. Proficiency in scientific programming (e.g. in python). Excellent oral and written English language skills. Strong collaborative skills, team spirit and
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to damage detection, condition monitoring, and forecasting. You are proficient in Linux and Python, preferably with experience in PyTorch or a similar deep learning framework, and you are able to design and
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data science. You must be curious and driven with excellent interpersonal skills and writing competencies. Experience with programming languages, notably Python or R, is expected. The work will imply