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algorithms. Our research integrates expertise from machine learning, optimization, control theory, and applied mathematics, spanning diverse application domains such as medicine, energy systems, biomedical
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large family of membrane proteins involved in numerous physiological processes. By leveraging machine-learning enhanced virtual screening, the PhD student will be able to perform searches for ligands in
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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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application, which project you are applying for. Project 1: Understanding macrocycles’ cell permeability by machine learning. The objectives of this subproject are to use machine learning to develop fast and
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. Our research integrates expertise from control theory, machine learning, optimization, and network science, spanning diverse application domains such as energy systems, biomedical systems, neuroscience
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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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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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sense and includes development of new methods (especially using AI (artificial intelligence) and machine learning), structure-based calculations, and analysis of large-scale data in life sciences. Duties
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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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the work at the university. Qualifications Required: Higher-education degree including subject specialisation (in-depth study of a subject) in computer science, computer systems or information technology