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combining two of Europe's new satellite sensors. If you have interests in physics, climate and machine learning, this is the Doctoral student position for you! About us Our team is part of the Division
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-performance computing. SLU provides access to extensive datasets that can be used to develop machine learning methods and automated analyses relevant to the position. Long-term datasets are available from, i.a
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machine learning. The employment This employment is a temporary contract of two years with the possibility of extension up to a total maximum of three years. The employment is full-time. Starting date by
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evolution across different genomic regions by developing interpretable and efficient methods in comparative pangenomics, leveraging machine learning methods and statistical analysis (https://cgrlab.github.io
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from talented and highly motivated candidates to pursue a PhD in machine learning at KTH, Sweden, and NTU, Singapore. This is a fully funded, joint doctoral position that will lead to a joint PhD degree
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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 temporal data
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multi-modal perception and machine learning. Current noninvasive agricultural monitoring systems rely primarily on passive sensing, which limits sensitivity to early-stage plant stress. This project
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The applicant must: hold a PhD in a relevant field (e.g. computer science, artificial intelligence, machine learning, computer vision, animal science, biology, veterinary medicine, or a related discipline) have
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degree in machine learning. The successful candidate will be supervised by professor Aristides Gionis (https://www.kth.se/profile/argioni/ ). The research team focuses on developing novel methods
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, machine learning, etc. Building a quantum computer requires a multi-disciplinary effort involving experimental and theoretical physicists, electrical and microwave engineers, computer scientists, software