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Leibniz-Institute for Plant Genetics and Crop Plant Research | Neu Seeland, Brandenburg | Germany | 5 days ago
architecture of important crop traits like grain yield heterosis. In the era of large population size and dense genomic data such as whole-genome sequencing, new algorithms are needed to remove the bottleneck
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learning (ML) methods—including surrogate modelling, feature extraction, and inverse design algorithms Generate synthetic microstructures (based on the open-source OptiMic software) Perform descriptor
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available sensor and meter infrastructure, affordable computational resources, and advanced modeling algorithms. MPCs excel in handling constrained optimizations and new operational conditions, whereas RLs
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for Science & Technology (KAIST), and an external stay at KAIST will be included as part of the PhD program. Qualifications Proficiency with Python Experience implementing various Machine Learning algorithms
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Europe | about 1 month ago
manufacturing, development of machine learning algorithms and design of optical communication networks or power consumption and energy saving. The synergies of MATCH consortium act together to enable the thirteen
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of scientific data, e.g. from image acquisition modalities or scientific simulations. Efficient algorithms are at the core of most of these data analysis and visualization applications. The focus of this Ph.D
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variants of importance sampling. We will connect these methods to modern formulations of Monte Carlo algorithms to improve their accuracy, scalability, and overall computational cost. The methodology so
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Dortmund, we invite applications for a PhD Candidate (m/f/d): Analysis of Microscopic BIOMedical Images (AMBIOM) You will be responsible for Developing new machine learning algorithms for microscopy image
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and innovation catalyst, in this exciting project, you will develop novel algorithms to monitor and analyse workers' movements, detect harmful movement patterns, and implement simple intervention
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formulation, which displays striking similarities to that used by the Computational Fluid Dynamics (CFD) community, has inspired the investigators to adopt conventional CFD algorithms in the novel context