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characterize large quantities of candidate molecules, calibrating theoretical models with experimental data, predicting promising candidates with computational tools and machine learning algorithms, and
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promising candidates with computational tools and machine learning algorithms, and elucidating structure-property relationships of emerging molecules, polymers, solid-state materials, formulations, etc. Tasks
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are designing new artificial intelligence algorithms for a self-driving car. You seek out diverse voices to highlight different experiences that uphold our commitment to equity and inclusion. Outside of long-form
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and Abilities Knowledge and experience with bioinformatics algorithms and genomic data analysis pipelines Knowledge of software for genomic data analysis Education, Certifications, Licenses Education
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will also have the opportunity to contribute to algorithm development, software architecture design, and software implementation. The ideal applicant for this position will have several characteristics
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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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. Familiarity with omics approaches, including genomic, transcriptomic, and metabolomic analyses. Experience with developing and applying machine learning algorithms to analyze biological data. Application
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digital communication protocols, and applying advanced Digital Signal Processing (DSP) and Machine Learning algorithms on embedded systems. The Research Assistant 2 will report to the McGill Principal
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. 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