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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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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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machine learning, with a particular focus on distress monitoring. Faculty of Science The Faculty of Science (FNWI), part of Radboud University, engages in groundbreaking research and excellent education
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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
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, mythical scenes of making, or depictions of making in sacred or military contexts). They will take into account (where applicable) how images interact with epigraphic frames and contexts of production and
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of making in ancient Roman visual culture (e.g. depictions of craftsmen at work, mythical scenes of making, or depictions of making in sacred or military contexts). They will take into account (where