301 phd-studenship-in-computer-vision-and-machine-learning Postdoctoral positions at Nature Careers
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Postdoctoral studies in single-cell and computational biology Do you want to contribute to top quality medical research? A postdoctoral position is available in the laboratory of Professor Francois
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(data assimilation, machine learning, etc.) Writing proposals / securing external research funding Writing and submitting scientific papers Leading a research group Supervising students Participating in
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working at the boundaries of several research domains PhD degree in computational biology, bioinformatics, systems biology, bioengineering, chemical engineering, or a related discipline Knowledge and
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and analysis of mathematical methods for novel imaging techniques and foundations of machine learning. Within the project COMFORT (funded by BMFTR) we aim to develop new algorithms for the training
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iNANO at Aarhus University is seeking a postdoctoral fellow for the Novo Nordisk Foundation CO2 R...
Do you have a passion and vision for developing new platforms for scalable microbial electrosynthesis of CO2 to methane? Come and be part of the team of Profs. Alfred Spormann at the NNF CO2
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)! Tübingen has a long history of academic excellence (founded in 1477; DNA was discovered here ; linked to 11 Nobel laureates) and is an innovation center in medicine and machine learning. About Eberhard
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during infection using machine learning Detailed information on and specific requirements for each project is given below. The IceLab Multidisciplinary Postdoctoral Program funded by Kempestiftelserna
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Ph.D. or equivalent degree in mathematics, physics, computer science, bioinformatics, or a related field Experience in developing deep learning models Ideally, prior experience in analyzing biological
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ERC-funded postdoctoral fellow in theoretical developmental biology, using tools from applied mathematics, biophysics, and machine learning A talented and creative researcher is sought to take part
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highly interdisciplinary, integrating big data analysis, state-of-the-art machine learning models, mathematical modeling, and systems biology to elucidate the mechanisms of drug interactions in complex