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Skip to main content. Profile Sign Out View More Jobs Part-time Assistant in Management Science – DTU Management Kgs. Lyngby, Denmark Be the First to Apply Job Description We are looking for a part-time assistant with a background in programming and decision science to support research...
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large datasets acquired under challenging conditions. This allows us to gain statistically relevant data and map out the influence of external parameters while working under relevant conditions. In situ
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with a range of multi-omics data types and associated bioinformatic algorithms/statistics. Familiarity with methods for computational modeling of metabolism (constraint-based, kinetic modeling, etc
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methods and techniques to study and characterize sound and vibration on the millimetre and sub-millimetre scale as well as statistical methods to evaluate metrological aspects such as reproducibility
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Preferable knowledge of statistical and machine learning methods You must have a two-year master's degree (120 ECTS points) or a similar degree with an academic level equivalent to a two-year master's degree
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include: Create a digitalized platform for sustainable lifestyles, bringing together the individualized household data from Danish statistics on household consumption and spending, including energy
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academic environment spanning the science disciplines mathematics, statistics, computer science, and engineering. We conduct research of high international standard that is at the forefront with
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preferred candidate shall have background / experience in the following areas: Solid Mechanics Crystal Plasticity Fracture Mechanics Statistics Programming experience in languages such as Fortran or Python As
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in laboratory analyses of biota, sediment samples, etc. Statistical analysis & methods. Innovative approach: A strong commitment to innovative research, both in advancing scientific knowledge and in
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Kristoffer Negendahl. We offer an opportunity to develop expertise in various domains, such as advanced building physics simulation techniques, machine learning approaches and statistical modeling