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for multimodal inferences, combining computer-vision, environmental parameter measures and DNA data. Your role will be central in data acquisition and foremost machine-learning models creation. You will
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. Implement Advanced Characterization Techniques to evaluate material performance and device reliability, integrating electronic control systems including DC-DC voltage boosting and maximum power point tracking
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analyzing large-scale datasets using software tools like Python Proven track record of publishing high-quality articles in peer-reviewed journals Excellent command of written and spoken English Familiarity
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Excellent skills in statistical modelling, preferably using R Proven track record of publishing in international peer-reviewed journals as first author Willingness to conduct fieldwork and participate in
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ecology, macroecology, metacommunities, and/or analysis of ecological time series. The applicant should have a strong track record of scientific publications. Over the past years, we have compiled one
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equivalent degree in agriculture, biology or any related field. You are an early career researcher not more than one year after your PhD and you have a proven track record and extensive experience with genetic
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). The empirical research should capture and analyze teaching and learning processes, for example by video analysis or eye-tracking. Development activities for instance may include AI tools, the creation
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Competency in visualizing and analyzing large-scale datasets using software tools like Matlab, IDL, Ferret, Python, or R Merit criteria: Proven track record of publishing high-quality articles in peer-reviewed
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on two core but complementary areas: Computer vision and sensor data analysis, applied to tasks such as object detection in drone images (e.g., pest or disease detection), object tracking (e.g. leaves
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criteria: Proven track record of publishing high-quality scientific articles in peer-reviewed journals. Excellent command of written and spoken English. Proficiency with advanced statistical methods