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for this position will work as a member of an interdisciplinary team led by Dr. Colin Xu on Department of Defense (DoD)-funded research project involving the use of statistical and machine learning methods
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Remote Sensing; Machine Learning Models for Predicting Wildfire Spread; Wildfire Risk Assessment Through Multi-Modal Data Integration; Automated Vegetation and Fuel Load Mapping Using Computer Vision; AI
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the Institute of Psychiatry, Psychology, and Neuroscience (IoPPN) in KCL. The candidate is required to: Have a PhD in Signal Processing, Machine Learning, or Bioengineering from a reputed university. Have a
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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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the theoretical foundations of machine learning itself, leveraging tools from quantum information, statistical mechanics, and complexity theory to understand and enhance modern AI. Successful candidates will work
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, Python, Bash). Good level on machine learning. Good level of written and oral English. Ease in a multidisciplinary environment, taste for teamwork, interpersonal skills. Scientific curiosity
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it comes to running experiments an affinity to AI technology (incl. machine learning, computer vision) (German) permit to perform animal experiments (equivalent to FELASA B or C) desirable previous
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neurological departments of Lyon's hospitals. Lyon is a vibrant and beautiful city, just 1 hour from the French Alps by car, and 2 hours from the Mediterranean coast and from Paris by train. More on our work can
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Computer Engineering or a closely related field, completed by the start date of the appointment. U.S. citizenship is required. Research experience in at least one of the following areas: ● Chip design
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, biomedicine, and other areas of societal importance. Coding and/or machine learning experiences are highly valued. Specific projects may involve developing multiscale simulation methods for quantum mechanical