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management. Preferred Qualifications: Ph.D. in Plant Breeding/Genetics, Genomics, or related field, familiarity with plant breeding programs, proficiency in R and/or Python, experience with genome-wide
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Django Python development. Experience with VCS, preferably Git. Advanced skill level in CSS, HTML5, and JavaScript. Knowledge of MVC and responsive web designs. Knowledge of application testing and
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spectrometry. • Experience preparing research documentation such as manuscripts, grant applications, or regulatory submissions. • Proficiency in statistical analysis and/or coding (e.g., Python, R) for data
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(even at a theoretical or modelling level) with neuromorphic hardware architectures or integrated photonics for signal processing. Proficiency in Python and/or C++, and familiarity with machine learning
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architectures (e.g., convolutional neural networks or transformers); deep specialization is not required. Exposure to Generative AI concepts and large language models (LLMs) is a plus. Proficiency in Python
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or more relevant platforms or technologies, such as: Copilot Studio o Power Automate / Power Apps, Power BI and semantic data models, Dataverse or relational databases, APIs, REST/JSON integrations Python
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University of New South Wales | Canberra, Australian Capital Territory | Australia | about 20 hours ago
maintaining application components Proficiency in one or more programming languages (e.g. Java, Python) and working with shared codebases Sound understanding of software development practices, including version
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Knowledge in the field of Machine Learning, including training, inference, and optimisation of transformer architectures. Knowledge in the field of ML security is desirable. Good Python skills, especially
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crystallography. More information can be found on their website: https://mcb.berkeley.edu/labs/meyer/index.html What We Provide: One-on-one mentorship with a senior research scientist while working collaboratively
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: https://www.list.lu/ How will you contribute? You will participate in applied projects in the framework of the risk evaluation of micropollutant contamination in surface and groundwaters, involving