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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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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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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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). 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
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area (e.g. biochemistry, molecular biology, molecular neuroscience) and have a strong track record of accomplishment. Candidates with proven interests and/ or experience in biochemistry or organelle
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or cellular biology) and have a strong track record of accomplishment. Candidates with proven interests and/ or experience in molecular neuroscience and/ or organelle proteomics and/ or the use and application
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learning settings with a specific focus on museums. The lab uses a variety of methods (e.g. eye-tracking, VR) covering the entire continuum from computer-based tasks to real-life engagement. With the aim