50 coding-"https:"-"FEMTO-ST"-"CSIC" "https:" "https:" "https:" "https:" "https:" "https:" "P" positions at Monash University in Australia
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. The candidate will be also involved in studies analysing immune responses to P. falciparum malaria vaccines. Candidate Requirements Applicants will be considered provided that they fulfil the criteria for PhD
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We have a range of potential research projects on offer in partnership with VIFM - https://www.vifm.org/ - looking at ML techniques in predicting forensic diagnoses / image analysis, across
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Epilepsyecosystem.org as a way to bring researchers, code and data together from all over the world to help solve the problem of seizure prediction. Required knowledge Machine learning, AI, signal processing, dynamical
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contrastive self-supervised learning task to learn from massive amounts of EEG data. Frontiers in human neuroscience. [2] https://www.emotiv.com
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nucleosynthesis in violent episodes suffered by ancient stars" (with Dr Carolyn Doherty) "Applying 3D stellar hydrodynamics findings to 1D stellar codes: Improving the modelling of convection in stars" web page
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/C++) computer codes implementing a cryptographic algorithm. Although desired, background in advanced cryptography is not a must. Application of a PET algorithm to solve a real-life problem: This
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fee waived. Please request a waiver code from asiascholarship@monash.edu before you submit your course application. More information and entry requirements for international undergraduate courses 2025
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locations in hundreds of example video frames. Coding expertise is also needed. These barriers mean that automated behavioural prediction tools are accessible to only a fraction of behavioural research labs
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this scholarship: Students must maintain full-time enrolment and satisfactory academic performance as determined by the faculty. The Unit Codes: BEX3024 Industry Based Learning Project Unit BEX3012/3112 Work
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Scientific. The American Statistician. 2019;73(1):69-81. O'Hagan A, Buck C, Daneshkhah A, Eiser J, Garthwaite P, Jenkinson D, et al. Uncertain judgements: Eliciting experts' probabilities: Wiley.; 2006