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Position as Computational Analyst / Bioinformatician in RNA Therapeutics and Cardiometabolic Disease
datasets. Proficiency in at least two of the following programming languages: Python, R. Experience in Machine Learning and Computational RNA Biology are desirable. Hands-on experience or understanding
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, preferably with a background in behavioral neuroscience or signal processing (especially of bioacoustics data) - Programming experience (e.g. Python, R, Matlab, etc.) - Strong communication and
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relevant field. In-depth knowledge and experience with advanced data processing and statistical analysis in R; knowledge and experience in spatial modelling, machine learning, or computational methods
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offer 1-2 specialist positions aimed at increasing the support for SDU’s researchers in developing and winning European collaborative R&I-projects. We hope to hire one junior research supporter on a two
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experience with R, MATLAB or others will also be considered an advantage). Strong communication skills with experience in scientific writing and presenting research to diverse audiences. Able to lead in
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Willingness to engage in application-oriented R&D, by closely interacting with partners Contact information For further information, please contact Professor Christian Schlette via email (chsch@mmmi.sdu.dk
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programming skills in Python and/or R Familiarity with machine learning frameworks (e.g., PyTorch, TensorFlow, scikit-learn) Excellent problem-solving, organizational, and communication skills Demonstrated
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Bioinformatics Staff Scientist at the Center for Adipocyte Signaling (ADIPOSIGN) (Academic Employee)
Experience with database development, such as SQL-based databases, is considered a plus. Proficiency in at least one of the following programming languages: Python or R Experience in designing web interfaces
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a team. Willingness to engage in application-oriented R&D, by closely interacting with company staff. Exceptional problem-solving skills and the ability to think creatively and innovatively. Self
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, Statistics or other fields related to Epidemiology or Environmental Medicine. The ideal candidate has prior experience in working with health data, has previously worked with R or similar statistical analysis