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contribute to cargo selectivity. The position is for a period of four years. The nominal length of the PhD programme is three years. The fourth year is distributed as 25 % each year and will consist
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scientific information for distribution to resource managers or other interested audiences. Learning Objectives: Participants will learn about: Conducting user-engaged science Invasive species concerns and
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power engineering. In condition monitoring non-invasive data is analyzed through machine learning algorithms or by statistical methods. The aim of predictive analysis is to use non-invasive methods
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environment. The mission of the center is to adopt a transdisciplinary approach to advance research and education in quantum computing and algorithms, quantum materials for next-generation technologies, and
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algorithms. The candidate should have the following skills: • Strong technical, analytical, and quantitative abilities; • Strong interpersonal, organizational, and communication skills, and the willingness
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with expertise in biology, biotechnology, computer science, microscopy and bio-engineering that is developing new microscopy hardware and new computational algorithms for the encoding and decoding
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, molecular properties, and pathological images. Strong knowledge and experience in data science algorithms, methods, and analysis techniques. Experience in programming using Python and R languages and working
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Institut national de la recherche scientifique (INRS) | Varennes, Quebec | Canada | about 6 hours ago
. Responsibilities include (but not limited to): Lead the development of the NC-ARPES technique (hardware, post-processing algorithm, theory, data interpretation) Propose and perform new TR-ARPES studies of quantum
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with rare, resistant, or genomically un-targetable cancers. Responsibilities Design and evaluate algorithms for treatment and response matching using integrated clinical and molecular datasets. Develop
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the prevalence and risk of modern slavery. There will be a focus on Bayesian nonparametric methods and practical development of MCMC algorithms that can be applied to data. Translating the project findings