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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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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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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
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Engineering, Machine Learning, Artificial Intelligence, Computational Linguistics, or a related field) • Strong programming skills (e.g., Python) • Strong skills in machine learning, deep learning and modern
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, Python and/or R, and ability to manage and structure large datasets is essential. Interest/skills in application of AI methods to clinical data is an advantage. Stipend 2: Genetic Risk Communication and
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skills, Experience in programming in Python or another language, e.g., in C++, Matlab, R, Familiarity with basic concepts of dynamical systems, Knowledge of wind turbine dynamics is a plus, Curiosity to
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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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- Significant experience in programming, e.g., in C++, Python or Matlab. Experience with quantum simulators, such as NetSquid, is a plus; - Familiarity with the basic concepts of quantum information and
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, or Python. You will also be able to shape your own research. This includes primary data collection through surveys, or qualitative or quantitative interviews. Working as a PhD student requires the ability