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- Knowledge in programming in Python or R - Familiarity with machine learning or deep learning methods is a plus - Interest in plant genomics, evolutionary biology, or comparative genomics - Proficient in
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strong background in machine learning and/or computer vision is required, along with solid programming skills in Python and experience with deep learning frameworks (e.g. PyTorch). Prior research
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computational scientific workflows. Experience with scientific programming (Python or similar) Experience working in Linux-based computational environments Documented experience with high-performance computing
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competence in programming with Python and working on Linux based platform is essential for this position, as is a background in either Mechanical Engineering, Material Science, Applied Physics and/or similar
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proficiency in scientific programming (e.g. in python) and must demonstrate the ability to handle large amounts of data confidently and systematically. Previous experience with machine/deep learning will be
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or several of the following areas: A solid foundation in programming and system development, particularly using Python and machine learning frameworks. Should be comfortable working with machine learning
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laboratory work (DNA extraction, PCR, sequencing workflows) Bioinformatics and statistical analysis (e.g., R or Python) Biodiversity assessment or ecological community analysis Fieldwork experience in
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. The candidate should ideally have experience or interest in one or several of the following areas: A solid foundation in programming and system development, particularly using Python and machine learning
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computations using the Python-based Taskblaster workflow framework. CAMD offers an international and scientifically stimulating working environment at the Department of Physics, DTU, located in the northern
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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