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and Liu, Supervised learning in physical networks: From machine learning to learning machines, PRX 11, 021045 (2021) [2] Stern and Murugan, Learning without neurons in physical systems, Ann Rev Cond
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their surfaces. Machine learning methods are used to close the complexity gap. Currently, the group consists of three full professors, one associate professor, 6 postdocs and about 15 PhD and 7 master
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is also essential. Must be computer literate with proficiency and working knowledge of database and reporting tools such as Microsoft Word, Excel, and PowerPoint. Should have competence in basic cell
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of results at conferences - interaction with team members and international collaborators The Machine Learning for Integrative Genomics team (https://research.pasteur.fr/en/team/machine-learning
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to see the following publications for some context: https://pubs.rsc.org/en/content/articlehtml/2024/gc/d4gc00765d https://pubs.rsc.org/en/content/articlehtml/2024/gc/d4gc04533e https
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Science, Computer Engineering, or a related field, completed before the fellowship start date. Candidates should demonstrate experience in at least two of the following areas: compilers, parallel programming
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Appointment Term: 1-2 years depending on performance and availability of funding Appointment Start Date: Summer/ as soon as possible Group or Departmental Website: https://sparklab.stanford.edu/ (link is
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or by car (parking available). Public transportation costs are partially covered. Where to apply Website https://emploi.cnrs.fr/Offres/CDD/UMR7104-MARMEN-001/Default.aspx Requirements Research
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and simulation Prior experience with particle accelerators and/or FELs is highly desirable Familiarity with machine learning techniques is a plus but not necessary Excellent command of English is
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, gender, gender expression, gender identity, genetic information, national origin, race, religion, (including pregnancy and pregnancy related conditions), sexual orientation, or military status. Duke