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initiated to explore how machine learning and the combination of different types of data (optical and electrical) can be utilized to improve decisions during on-going experiments as well as its potential
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. Intrusion Detection Systems (IDS) are critical components of an effective IoT cybersecurity defense strategy and machine learning plays a pivotal role in intrusion detection by learning from past and
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subject. Completed project in single cell analysis of cell painting data. Experience with multivariate methods, supervised and unsupervised machine learning including deep neural networks and self
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doctoral students active on both campuses. Learn more about the Department here: https://www.uu.se/institution/arkeologi-och-antik-historia . Center for the Human Past The CHP is a pioneering initiative that
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applies finite-field density functional theory (DFT)-based and machine learning (ML)-accelerated molecular dynamics (MD) simulations recently developed and applied in the TeC group at Uppsala (https://tec
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successful research and education in these areas - renewable energy sources, electric vehicles, industrial IoT, 5G/6G communication, machine learning and wireless sensor networks as well as smart electronic
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to independently analyse data and to develop scientific texts on a scientific basis Knowledge of advanced statistical and machine learning techniques Experience in developing data visualization. Additional
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PhD degree in economics with broad experience in conflict research. The PhD degree needs to be obtained by the time of the decision of employment Excellent skills in quantitative methodology and machine
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translatable drug and therapeutic targets in human tissue cohorts. The team leverages technologies including spatial transcriptomics, single-cell sequencing, and machine learning. The person selected