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Massachusetts Institute of Technology (MIT) | Cambridge, Massachusetts | United States | 3 months ago
substantial intellectual independence and opportunities to collaborate closely with machine learning scientists developing predictive models of cellular function. Machine learning experience is not required
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of generative AI tools, use of large language models, machine learning, and ethical frameworks for AI implementation. Ability to apply AI to interdisciplinary research or developing AI models
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the computational task: first-principles active-site descriptor models, high-throughput screening with machine-learned interatomic potentials, and a dedicated catalysis database. This position is ideal for a
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demonstrate how innovative methodologies can generate new knowledge within art history. Relevant topics may include, but are not limited to: Artificial Intelligence and Machine Learning in Art History Museums
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the European Union through the COMPETE 2030 Programme, of Portugal 2030, under the following conditions: Scientific Area: Machine Learning Admission requirements: Candidates who cumulatively meet the following two
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. Position Overview The successful candidate will develop and apply advanced computational and machine learning methods to large-scale genomic, clinical, and imaging datasets, working across one or more of the
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scales, from the genome to the continent, and sub-daily to evolutionary time scales. One of the goals of the SCINet Initiative is to develop and apply new technologies, including AI and machine learning
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therapeutic targets. Develops and applies machine learning and statistical modeling approaches for biomarker discovery, classification, prediction, and patient stratification. Develops algorithms and
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skills, and proficiency in machine learning frameworks such as Python, C/C++, TensorFlow, and PyTorch. 4. Hands-on experience with ultrasound systems, particularly Verasonics or similar platforms is
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geoscientific process models, as demonstrated by presentations, publications and/or repositories Expertise in applying Bayesian statistical methods, machine learning methods, or related statistical inference