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4-year PhD fellowship in the Research Programme - Deep Learning-Accelerated Crystallography Pipeline
We welcome applications from candidates with a broad range of academic backgrounds and experiences for a 4-year PhD project on Mathematical and Machine Learning Aspects in Crystallography at Durham
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on machine learning and networks, release the code in open access and actively participate in the international interpretability community. Where to apply Website https://seuelectronica.upc.edu/en/procedures
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collaboration with internal and external partners, predictive models including machine learning (ML), materials thermodynamics, etc. for high-throughput identification and screening of advanced materials for gas
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that truly understands its environment. You have a master's degree in Computer Science, Artificial Intelligence or similar. You are interested in Logic, Machine Learning, Knowledge Graphs, Stream
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? As a machine learning researcher, you will initiate and work on innovative data-driven solutions in the bioeconomy and food domains. One example is improving fermentation screening for plant‑based
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Are you a MSc graduate with background in data science, computer science, biostatistics, bioinformatics or a related field? Do you have a solid foundation in machine learning? Are you passionate
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, computational biology, statistics or a closely related field. You have strong programming skills, preferably in Python, and experience with machine learning or deep learning. Experience in computer vision
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of response and develop predictive models. The work will involve analysis of large-scale datasets through multiomics integration, machine learning, statistical genetics, QTL analysis and development of genetic
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the quality and availability of donor organs is one of the greatest challenges in modern transplantation. At the University of Groningen and UMCG, you will contribute to the development of next-generation
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realistic oxide nanoparticles with sizes from a few 10s of atoms to several 1000s of atoms. In each case, machine learned interatomic potentials (MLIPs) will be developed to describe their structure and