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Field
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relevant to medical science and with strong foundations in statistics or machine learning and hands-on Python experience with superior programming skills; prior knowledge of causal inference and clinical NLP
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- or nanoplastics, online or offline ice nucleation experiments. A good knowledge of programming languages such as Python, R, MATLAB or IGOR is expected. Excellent English skills, both in verbal and written
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should have documented background in the following areas: Electrodynamics Data analysis for scientific applications Programming (e.g., Matlab, Python, C, C++) for scientific applications Previous
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with data analysis/modelling and programming (R or Python). Advantageous: geostatistics, digital soil mapping, remote sensing, GIS, big data or cloud tools. Proactive working style, strong communication
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automated manner. This includes the development of Python-based control and automation routines, as well as the analysis of large experimental datasets. These characterization techniques may be complemented
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Telecommunications or Computer Science. • Programming skills (preferably in Linux environments, Python, C/C++, React, TypeScript, Node.js, Next.js, D3.js or equivalent). • Knowledge of cloud computing (e.g. AWS
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(required) quantitative skills in time-series analysis, biogeochemical data processing, and uncertainty assessment (R, Matlab, Python, or similar) (desired) excellent English communication skills and the
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relevant field (Informatics, Computer Science, Ecology, or Biology). strong Python programming skills proven experience with Machine Learning and image processing (Deep Learning/Object Detection) strong
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, and cybersecurity. Programming skills (C, C++, Python, or Matlab). Eager to work within a team and independently. Ability to collaborate with industry and academic researchers. Fluent in spoken and
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academic track record proficiency in programming (preferably in Python), personal characteristics, such as a high level of creativity, thoroughness, and/or a structured approach to problem-solving