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or Mechanical Engineering – strong background in machine learning, deep learning and / or computer vision – good programming skills in Python (and C++) – basic knowledge of optics including concepts like PSF, MTF
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field such as computer science, bioinformatics, mathematics, computational life sciences, or related. Profound knowledge in machine learning, preferably deep learning for image data. Experience in
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collaborate closely with a dedicated team of soil fauna experts, ecological data modelers, computer-vision system engineers. Your Tasks Establish data science pipelines, data-modelling strategies, model
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Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig | Leipzig, Sachsen | Germany | 3 months ago
Scientific university degree in neuroscience, psychology, biomedicine, computer science or a related field Experience with imaging procedures (e.g. MRI, fMRI pre-processing) Thorough knowledge of data
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of computer science or using computer vision methods Excellent knowledge of the development and implementation of methods in the field of digitization, artificial intelligence, machine learning and/or 2D/3D imaging and
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this knowledge gap and establish improved GHG models accounting for soil invertebrates. To achieve this, we create a rich AI-training dataset for multi-modal inferences, combining computer-vision, environmental
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Max Planck Institute for Multidisciplinary Sciences, Göttingen | Gottingen, Niedersachsen | Germany | 3 months ago
of critical thinking Experience with photonics, fluorescence, imaging or biochemical methods is helpful Our offer Highly motivated team Flexible working hours World-class institute with outstanding
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, memristive devices), and the evaluation with e.g. machine learning and image processing benchmarks Requirements: excellent university degree (master or comparable) in computer engineering or electrical
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completed your doctorate in industrial ecology or energy systems analysis You hold a very good degree (master`s or equivalent) in engineering (e.g., energy technology, mechanical engineering, process
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imaging techniques developing computational codes to process and understand large experimental datasets (e. g., image processing) analyzing experimental results; developing conceptual models and