153 phd-studenship-in-computer-vision-and-machine-learning Postdoctoral positions in Sweden
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of mathematical areas. The position will be placed at the Department of Computer Vision and Machine Learning (CVML) at the Mathematics Centre (https://maths.lu.se/). Mathematics Centre is a department
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, research and teaching are conducted in computer science, computer engineering, and informatics. We have two expanding departments in these areas, with around 50 employees. Your placement will be in
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-of-computing-science/ Project description and working tasks The project will develop privacy-aware machine learning (ML) models. We are interested in data-driven models for complex data, including high
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, military service, or similar circumstances, as well as clinical practice or other forms of appointment/assignment relevant to the subject area. The successful candidate must hold a PhD in one of
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organisations, military service, or similar circumstances, as well as clinical practice or other forms of appointment/assignment relevant to the subject area. Postdoctoral fellows who are to teach or supervise
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computational costs by orders of magnitude and enabling breakthroughs in drug design and materials science. The position bridges machine learning and molecular science, with opportunities for collaboration
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data and clinical information. Applicants must hold (or be close to completing) a PhD in a relevant field and have expertise in modern computer vision and AI research. Experience with biomedical data
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for materials science, and advanced optimizers for modern deep learning. The research may be conducted in collaboration with the Electronic and Photonic Materials and/or the Computer Vision Laboratory
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of PhD and MSc students, teaching and supporting in acquiring funds for future research projects from research funding agencies/councils, EU framework program or industry. Qualifications Eligibility
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at least 1 million DNA barcodes. The project involves collaboration with a computer vision lab at Linköping University, focused on developing AI-assisted techniques for picking out specimens for genome