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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
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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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Fundacio de Recerca Clinic Barcelona-Institut d'Investigacions Biomediques August Pi i Sunyer | Spain | 7 days ago
or ELISA. Scientific curiosity, eagerness to learn, and strong teamwork skills will be highly valued. Research group: Cellular Immunotherapies for cancer, Sònia Guedan Project: Next-Generation CAR-T Cells
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machine learning for audio and acoustics - Research in experimental techniques in acoustic -- - Investigación en procesamiento de señales y aprendizaje automático aplicados al audio y la acústica
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advanced instrumentation (ACSM, Xact, SMPS…) and machine-learning techniques. The selected candidate will work in a multidisciplinary and international research environment, contributing to the assessment
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) Item 03: Proven experience in both programming (Python, R, etc.) and the use of data analysis tools, as well as artificial intelligence / machine learning techniques (punctuation: 22.5) Item 04
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, Environmental Sciences, Chemical Engineering, Data Science, Geology, or related disciplines. Specific Requirements Programming skills interest in machine learning techniques. Ability to work in an organised
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ReDiLEEP training in response diversity methods, data management, reproducible code, R/Tidyverse, machine learning and AI for ecologists, visualisation, evidence-based policymaking, science-policy
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learning techniques for tool health monitoring and prognosis. Dissemination of research results through scientific publications, conference presentations, and completion of a PhD thesis. Required skills
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Position Description The project will focus on the development and application of advanced data-analysis techniques for gravitational-wave science, including machine learning and deep learning