42 phd-studenship-in-computer-vision-and-machine-learning Postdoctoral positions at Leibniz
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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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qualifications and skills: You hold a Doctorate degree (Ph.D.) in data science, bioinformatics, computational biology, genetics, or a closely related field. You have a strong background in machine learning (e.g
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Postdoctoral Researcher (m/f/d) in Cognitive Psychology / Computational Neuroscience to be filled as soon as possible. The position is limited to 3 years and can be extended for another 3 years. Your task
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teaching and curriculum development. Your qualifications PhD in computer science, data science, applied mathematics, physics, or a related field. Strong expertise in machine learning and deep learning
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or a related discipline A solid background in climate and atmospheric sciences, and extreme weather ideally supported by knowledge of machine learning and time series analysis is of advantage, as is
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-processing, and machine learning textual analysis of the full text of policy documents. Qualitative content thematic analysis is envisioned to compliment structural topic modelling to identify strategies and
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, survey) or applied microeconometrics, and applied economics. You have experience with big data and machine learning methods? This would be a particular asset! With excellent English language skills, both
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industrial partner and support the overall scientific goals of the project. YOUR PROFILE PhD degree in chemistry, materials science, energy materials, or a related field. Experience with lithium-ion batteries
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species Perform bioinformatics and computational analyses to identify microbiome-lifespan relationships Apply rigorous molecular biology methodologies to elucidate mechanisms underlying microbiome effects
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Family-friendly working conditions (certificate “audit berufundfamilie”) Supporting of mobility with a jobticket for using the public transport Easy accessibility by public transport or car (including free