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great interest in interdisciplinary research with an eagerness to learn and develop new approaches. Candidates should hold a Masters or equivalent degree in (Bio-)Chemistry, Biology, or any related field
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-funded competence centre for Artificial Intelligence and Machine Learning. It now consists of more than 40 excellent research groups, both in the field of application-oriented Machine Learning and in
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interdisciplinary desire to learn and willingness to cooperate, openness for internationalization and diversity, very good verbal and written English communication skills as well as the absolute determination
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quantitative data analysis (e.g., econometrics, statistics, machine learning) a high motivation and the ability to work independently with a strong team orientation excellent spoken and written English and the
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, both written and verbal Knowledge of German and/or a willingness to learn Computer/programming literacy, for example in R, and/or software used in image processing (Adobe Photoshop, ImageJ etc.) Ability
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, workshops and e-learning opportunities per year for personal development, as well as a wide range of health and sports activities Remuneration according to TV-H, company pension scheme, child allowance and
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subjects, high interdisciplinary desire to learn, and willingness to cooperate, openness for internationalization and diversity, very good verbal and written English communication skills (good command
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qualifications A masters degree in neuroscience, physics, evolution and ecology, or related quantitative fields. Prior experience with Python or Matlab, and a willingness to learn analysis tools. Excited about
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skills work together, in order to learn from one another and generate new knowledge and new methods to create a better quality of life in our world. DWI offers you a wide range of possibilities to develop
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of the 15 months of data collection. An amazing opportunity to learn about the lives of wild Guinea baboons in their natural environment. An opportunity to gain new skills and gain proficiency in diverse