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written and spoken English. Fluent in a computer coding language (python or Matlab or C++ or etc). The Scientific environment We offer creative and stimulating working conditions in a dynamic and
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or prior experience with multilingual or low-resource NLP Programming skills in Python and proficiency in deep learning frameworks (e.g., PyTorch, TensorFlow) and their application in high-performance
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of the following qualifications: Extensive experience in programming using Python, R, or other languages Research experience in greenhouse gases, or ecological experiments Insight into global
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possible. It is strictly required that you have experience with: Scientific programming, preferably in python and/or MATLAB and/or C++ Derivation and implementation of finite element methods (FEM) in code
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(deep neural networks) Probability theory Computer vision Robotics Programming skills (Python, C++) and ML libraries (PyTorch, Tensorflow) Preferably, the candidate has experience with: Bayesian machine
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, or ensemble models (e.g., XGboost). Proficiency in programming languages commonly used in data analysis, such as R, Python, or C++ (or similar). Strong communication, collaboration, and presentation skills
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visualization, and AMR gene annotation Laboratory experience with MinION sequencing Proficiency in R, Python, and Unix (bash/shell) environments Experience working on HPC clusters (e.g., Computerome) Demonstrated
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or optimization. Proven experience with programming and data analysis tools. Fluency in Python, including experience writing data analysis scripts and model training code. Experience with scientific computing and
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. in C/C++, Python, Matlab and Qt). Strong mathematics skills and practical knowledge in 3D modelling. The candidates are also expected to have the following qualifications: Ability to work independently
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also be able to demonstrate excellent ability to code with or learn computer programming languages, such as C++, C#, Python, and/or Matlab. A desire to engage in cross-disciplinary research