62 machine-learning-"https:"-"https:"-"https:"-"https:"-"Simons-Foundation" Postdoctoral positions in Australia
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programming, with experience in AI, machine learning or data-driven materials research highly desirable · a strong publication record, excellent communication skills, and the ability to work
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who demonstrate the following will be highly regarded: experience working with spatial omics or spatially resolved data in tissue experience applying AI or machine learning methods to digital pathology
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, machine learning or other relevant fields experience working with marine robotic systems or the data they collect, including inertial, acoustic, visual and magnetometer data field experience deploying
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control code, leveraging advances in machine learning and real-time control fabricate robust instrumentation or photonic components for use at 8m-class and future ELT-class observatories perform lead
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AUSTRALIAN NATIONAL UNIVERSITY (ANU) | Canberra, Australian Capital Territory | Australia | 18 days ago
opportunities to those of all backgrounds and identities. For more information about staff equity at ANU, visit https://services.anu.edu.au/human-resources/respect-inclusion Application information
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AUSTRALIAN NATIONAL UNIVERSITY (ANU) | Canberra, Australian Capital Territory | Australia | 28 days ago
all backgrounds and identities. For more information about staff equity at ANU, visit https://services.anu.edu.au/human-resources/respect-inclusion Application information In order to apply for this
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consult their website for collective agreement https://www.aerum-amure.ca McGill University is strongly committed to diversity within its community and especially welcomes applications from racialized
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of sorghum agronomy, integrate agronomic, survey and decision data using qualitative, mixed-method, statistical and machine-learning approaches, and translate findings into transparent and practical
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dynamics, stability analysis, renewable energy integration, or related areas. Knowledge of data-driven analytics, machine learning, signal processing, or advanced modelling techniques relevant to power
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within a multidisciplinary research environment. Experience with quantitative genetics, Bayesian methods, machine learning, large-scale genomic datasets, single-cell omics or integrative omics