101 machine-learning-"https:"-"https:"-"https:"-"Computer-Vision-Center" Postdoctoral positions in Australia
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using Python, ASE and relevant DFT software analyse electrochemical free-energy landscapes and connect elementary-step energetics to catalytic performance apply machine-learning interatomic potentials and
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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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apply mixed-model statistical methodology, including genomic prediction, crop simulation and machine learning, to improve prediction of crop yield and related traits. Supervision and Researcher
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degradation and bioreactor performance. apply machine learning and optimisation to guide experiments and improve fungal process performance. design and undertake laboratory experiments with biological and
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the State or enrolled at one of their universities. For information consult the following: https://www.gwnsw.org.au http://www.afuwsa-trust.com.au/postgraduate-scholarships/ http://gwq.org.au/awards
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STATA are preferred but professional development can be provided where necessary. The successful candidates will work under the supervision of Professors Maria Inacio ( https://research.sahmri.org.au
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for New Zealand Citizens or Permanent Residents enrolled in honours or masters level study in mathematics, statistics, machine learning, or science that will establish eligibility for PhD study
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free-energy landscapes and connect elementary-step energetics to catalytic performance apply machine-learning interatomic potentials and microkinetic models where appropriate collaborate with
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AUSTRALIAN NATIONAL UNIVERSITY (ANU) | Canberra, Australian Capital Territory | Australia | 29 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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validate mechanistic and hybrid AI models of fungal growth, polymer degradation and bioreactor performance. apply machine learning and optimisation to guide experiments and improve fungal process