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strategy and machine learning plays a pivotal role in intrusion detection by learning from past and simulated attacks. Duties The objective of the position is to explore and provide privacy-preserving and
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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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interdisciplinary team at Uppsala University with extensive experience in bioinformatics, biostatistics and biochemical analyses. The research group has extensive experience applying AI and machine learning methods
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systems, and measurement signals. Additionally, you will analyze experimental measurement data and operator data to validate models and apply multivariate regression and machine learning algorithms to infer
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PhD degree, or a foreign degree equivalent to a PhD degree, in physics, materials science, computer sciences, or a similar area of science. The degree needs to be obtained by the time of the decision
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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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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