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Field
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Experience Experience developing research software using appropriate languages and environements (Python, Julia, Matlab) Knowledge of optimisation problem formulations and solution methods Experience of risk
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of research include integrating AI-enhanced weather forecasts with crop models, exploring tools to guide land allocation and nitrogen management, or developing dashboards that fuse multiple data streams
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hardware. Relevant programming experience developing, implementing, debugging, and maintaining applications with Python. Experience training ML models using large-scale and specialized hardware. Experience
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disciplines, are well versed in multiple approaches and styles of thought. The goal is for the students to be comfortable communicating across traditional boundaries, especially across the divide between
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research in TRL forward. Theoretical knowledge of, or experience with, machine learning such as representation and generative learning, and natural language processing. Programming skills, e.g. Python, Java
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/or human-computer interaction. Programming skills, e.g. Python, Java, or C++. Excellent command in English, verbal and written. Prior experience as a research assistant during (under)graduate studies
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. PV field system data analysis (time-series data analysis with JMP software and /or Python). Accelerated ageing procedures (IEC standards). PV module failure modes. Corrosion. Strong communications
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”, led by Associate Professor Valeria Vitelli. Successful candidates will work on Bayesian models for unsupervised learning when multiple data sources are available, mostly tailored to the case
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of the largest iPSC biobanks in the United States which will give us access to multiple cell lineages carrying mutation in multiple key genes of the cardiovascular system. Education Requirement: Ph.D. in
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structure of trees and forests. You will work across multiple ecosystems, including the Amazon rainforest (AmazonFACE experiment in Brazil), the wet tropics of Australia (Queensland Permanent Rainforest Plots