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network-based, graph modelling, dimensionality reduction or machine learning approaches You can process and derive novel insights from integrative analysis of multi-omic datasets Desirable but not required
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courses). - Proficiency with computer tools: R, Python, Bash, Perl, Java, SQL. - English: High-level language proficiency. - Willingness, ability to learn, and teamwork skills will be valued. - Experience
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also pursues five key scientific research directions: brain-computer interfaces and intelligent systems; brain decoding and primate models; medical devices and medical equipment; intelligent medicine and
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development of analytical solutions, data analysis and machine learning. Candidates should have a demonstrated record of scientific publications in international journals and participation in conferences
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, or a related field. Proven experience in machine learning, deep learning, generative AI and data mining. Strong programming skills (e.g., Python, R, MATLAB, or similar). Experience with data
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descriptors to support machine learning model development to accelerate materials discovery: Perform high-throughput DFT and molecular dynamics simulations to investigate the thermodynamic, structural, and
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sciences » Philology Chemistry » Physical chemistry Environmental science » Earth science Computer science » Computer systems Computer science » Computer systems Computer science » Computer systems Computer
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Machine Learning A PhD position is available at the Computer Vision Center (CVC) under the supervision of Fernando Vilariño and Paula García . The successful candidate will be enrolled in
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assisting with in-situ TEM measurements, facilitating cutting-edge research in sustainability and energy fields. Part of the project will also include the development of deep learning frameworks for TEM image
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integrating machine learning (ML) and molecular dynamics (MD) tools with experimental feedback, the project strives to accelerate the design of efficient and sustainable nanocatalysts, contributing