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Field
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computational methods and tools, including prior experience with algorithms relevant to computational biology, is a plus. ● Ability to work independently as well as part of an interdisciplinary team in a
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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 1 hour 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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use of data and algorithms. Excellent written and verbal communication skills and ability to communicate effectively with a variety of different stakeholders, e.g., academics, business executives
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development of mathematical models and algorithms for the analysis of biopharmaceutical manufacturing processes with a focus on assuring safety and alignment of machine learning models with the expected
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intermittent. The PhD will work will be twofold. The first part will be to improve and develop datasets and estimation algorithms for renewable energy that will enhance the simulation capabilities of the open
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Responsibilities: Development of stochastic and analytical methods for nonlinear partial differential equations Implementation of relevant numerical experiments using deep learning algorithms Job Requirements: PhD
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factors" - those key issues that must be addressed to achieve the established discharge plan; use of clinical pathways, algorithms, and clinical guidelines; use of specific protocols to address component
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has also been developing physics-based machine learning algorithms for three dimensional seismic modeling, imaging and inversion using high performance computation including parallelization on GPUs