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Field
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emerging to address the ‘broken system’ of antibiotic R&D. It focuses on the actors and stakeholders seeking to develop a radically altered antimicrobial innovation ecosystem by establishing alternative
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, including the development and use of 3D models and the study of extracellular vesicles Analyze high-throughput data sets using R or similar software Present research findings in meetings Document performed
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communication, and collaborative problem-solving capabilities, which are essential for success in innovation-driven environments. This training prepares graduates for impactful roles in semiconductor R&D
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analysis of large data sets, statistical modeling, and knowledge of at least one programming language (e. g.: R, Python and/or Julia) are required. Experience in machine learning and image recognition
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pressure Desired Practical experience in cell culture Practical experience in chemical analysis Experience in statistical analysis and programming languages, especially R What we can offer you Trust-based
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experience in chemical analysis Experience in statistical analysis and programming languages, especially R What we can offer you Trust-based working hours 30 days’ annual leave (5-day week) plus 24 and 31
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biomedical research field is emerging to address the ‘broken system’ of antibiotic R&D. It focuses on the actors and stakeholders seeking to develop a radically altered antimicrobial innovation ecosystem by
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experience with statistical tools (e.g. in R, MatLab, or Python) are expected. The team at DTU Aqua is highly international and knowing the Danish language is not needed. You must be available for boat-based
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experiences in working with remote sensing data, climate data and programming skills (R or Python) are desired. You enjoy working in an international team and you are keen on developing a key set of research
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(e.g., Python, R, MATLAB) Background or interest in nature-based solutions and optimization techniques Experience in working with climate model outputs and scenario analysis is an advantage A proven