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on methods such as functional connectivity analysis, brain network analysis, or machine learning; Excellent scientific writing and communication skills in English; Ability to work independently while
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of human behaviour, thinking, learning, and how people live together. We work on societal issues and problems that people experience in daily life. Central to this is individual and societal resilience and
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bulk and clonal protein expression data from large melanoma cohorts, integrate molecular, histological, and clinical data through machine learning (ML)/AI-assisted methodologies. Your expertise in ML
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, integrate molecular, histological, and clinical data through machine learning (ML)/AI-assisted methodologies. Your expertise in ML (Random Forest, SVM, Fully Connected Neural Networks) will be essential
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to the forefront of studying animal behaviour using machine learning, with a particular focus on distress monitoring. Faculty of Science The Faculty of Science (FNWI), part of Radboud University, engages in
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modelling, wavefront shaping, and machine learning algorithms. Information and application Are you interested in this position? Please send your application via the 'Apply now' button below before 15 June
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/ ) excels in teaching and research in the fields of human behaviour, thinking, learning, and how people live together. We work on societal issues and problems that people experience in daily life. Central to
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develop a simplified model focusing on the leader stage. You will: Analyze experimental data and microscopic simulations Identify relevant physical features and parameters Apply machine learning techniques
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As a postdoc, the following is required: You hold a PhD in computer science, epidemiology, econometrics, machine learning, artificial intelligence, mathematics, data science, medical informatics, or a
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PhD* in a topic such as animal acoustics, machine learning, quantitative ecology, quantitative biology, signal processing or similar. Evidence of the ability to conduct high-quality research and write