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-antibodies. You will focus on the identification of these antibodies by using mass spectrometry based de novo sequencing, machine learning and AI-tools to interpret the data. Your job The primary objective
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biology and bioinformatics, as well as in Machine Learning (including Large Language Models). Good understanding of evolutionary and molecular biology concepts, and good statistical (data analysis) and
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computational neuroscience, connectomics and/or Machine Learning is appreciated. Our goal perform connectome analysis on neuroimaging data\integrate connectome findings across multiple speices work on developing
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the following PhD project within its MKT research programme at ERIM: · PhD in Consumer Behavior · PhD in Quantitative Marketing · PhD in Fair Machine Learning in Marketing Other fully
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expert knowledge in a reusable format. Numerical Representation, Develop numerical representations of ship designs that are interpretable by machine learning algorithms and suitable for generative ai model
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processes associated with CIN [1], leveraging single-cell DNA sequencing understand CIN heterogeneity [2], and development and implementation of machine learning and AI models to imaging data [3]. The student
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transfer learning to transpose the recurrent neural network (RNN) model available for supercritical CO2 power cycles to other cycles. Since thermodynamic conditions vary greatly depending on the fluid and
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without neurons in physical systems, Ann Rev Cond Matt Phys14, 417 (2023) [4] Dillavou, Beyer, Stern, Liu, Miskin and Durian, Machine learning without a processor: Emergent learning in a nonlinear analog
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and kinematic models with machine-learning-based channel state information (CSI) prediction to enable robust, low-latency connectivity across multi-layer NTN systems. This PhD project sits
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-resolved prediction of wave elevation and wave loads in continuous time. Look into fusing the models into a foundational model. Setting up a synthetic training gym as a platform for AI model learning, e.g