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science/biomedical engineering or of relevant scientific field A solid background in machine learning Extensive experience with either computer vision or image analysis Good knowledge of deep learning
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into reliable information about structural and aerodynamic behaviour remains a challenge. The PhD will develop data-driven methods that combine measurements, physics-based models, and machine learning to extract
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Science, or a related technical field Master's or PhD degree in Machine Learning, Computer Vision, or related areas will be advantageous Preferred Qualifications: Experience with biological/ecological
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electrophysiology data obtained through collaborations and perform cross-species comparisons. We use machine learning techniques for neural data analysis and computational modelling with a special interest in
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Adaptive Learning in Brain-Robot Interactions School of Electrical and Electronic Engineering PhD Research Project Self Funded Dr Mahnaz Arvaneh Application Deadline: Applications accepted all year
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solver who wants to be part of a dynamic team. Learn more about the innovative work led by Dr. Don Ingber here: https://wyss.harvard.edu/technology/human-organs-on-chips/ What you’ll do: Independently
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–functional modeling of root system architecture. Phenomics data integration and high-dimensional trait analysis. Predictive breeding and quantitative genetic modeling. Machine learning approaches to genotype
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. Required PhD in Computer Science / AI / Machine Learning Strong publication record in AI, ML systems, or related areas Strong programming skills in Python, C/C++ and experience with PyTorch, TensorFlow, JAX
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engagement area, all aimed at creating a collaborative environment. University of Texas at Arlington Research Institute (UTARI) https://utari.uta.edu/ Center for Artificial Intelligence and Big Data (CARIDA
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, etc.) o Energetic frustration or protein energy landscape analysis o Machine learning in protein science o +2 years of experience after PhD Knowledge of evolutionary biology concepts (phylogenetics