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Field
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with physics-informed neural networks, automatic differentiation, neural ODEs, or other physics-aware DL techniques. Skill in programming languages such as Python, C/C++, Go, Rust etc. Ability to model
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that integrate simulation, machine learning, and data analysis. Numerical optimization methods (e.g. machine learning including deep neural networks, reinforcement learning, data mining, genetic algorithms
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extensive expertise in electrophysiology to join international research team "Astrocyte-Neuron Networks" lead by Prof. Dr. Cristina García Cáceres at the Institute for Diabetes and Obesity (IDO
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convolutional neural networks (CNNs) and large language models (LLMs) to analyze retinal and corneal data including visual fields, fundus photographs, optical coherence tomography (OCT) images and genetics data
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Differential Equations, and Graph Neural Networks. The objective is to measure and predict evolutionary forces and spatial cell interactions in healthy versus cancerous tissues, ultimately identifying
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(Random Forest, SVM, Fully Connected Neural Networks) will be essential for feature selection, model training, and biomarker ranking. Additionally, you will perform proteomics profiling of melanoma clones
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. Research Content: 1. Study the brain network mechanisms of deep brain stimulation neuromodulation. 2. Research on neural biomarkers and closed-loop neuromodulation strategies and methods. 3. Clinical
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and other machine learning models (especially neural network models, time-series models) and coding in python and R. Strong collaborative skills and ability to work well in a complex, multidisciplinary
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their expertise together to establish neural organoid models recapitulating aspects of neural-microglia interactions in neurodegenerative diseases at Ghent University. About project MINDFUL: Lipid accumulation in
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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