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PhD in computer science, applied mathematics, physics, computational biology or other quantitative disciplines. The ideal candidate has a machine learning background, with previous experience building large-scale generative models, complex pre-processing pipelines, and comprehensive benchmarks to support scientific tasks.
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PhD in Computer Science, Machine Learning, Computer Engineering, Applied Mathematics, Electrical Engineering or related fields. PhD focus on NLP or Masters with 5 years of industrial NLP research experience Multiple publications on topics related to the pre-training of large language models (e.g. technical reports of pre-trained LLMs, SSL techniques, model pre-training optimization) Member of team that has trained a large language model from scratch (10B + parameters, 500B+ tokens) Publications in deep learning theory Publications at ACL, NAACL and EMNLP, Neurips, ICML or ICLR.
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This individual will apply their skills in machine learning, image processing, computer vision, mathematics, optics, and software engineering to develop algorithms to be integrated into onto’s innovative inspection platforms.
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Experience with: multivariate statistics, (e.g., machine learning, multiple regression, structural equation modeling, trend analyses) program evaluation, survey research, data acquisition and organization for the purposes of building statistical models.
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As a member of our Machine Learning Research team, you will design new approaches to derive insights from Pfizer's proprietary data and external datasets to generate testable hypotheses across the drug discovery continuum with a focus on extracting biological knowledge.
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Solid foundation in finite element analysis, solid mechanics, and applied mathematics. Required Qualifications: PhD in Mechanical Engineering, Electrical Engineering, Ocean Engineering, Applied Mathematics, or Applied Physics (minimum.
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Job Description MIT Lincoln Laboratory seeks a highly motivated, detail-oriented, curious and driven physicist, applied physicist, or researcher in a related field with hands-on lab experience.
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Work at the intersection of experimental and groundbreaking digital technologies, with a particular emphasis on expertise in machine learning and artificial intelligence (AI) as applied to small molecule drug discovery.
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The Human-centered AI team in Digital Health Oncology R&D is looking for an Associate Director of Data Science to specialize in the development of novel digital endpoints using data science, statistical learning, and innovative machine learning methods on multi-modal datasets including clinical data, real world evidence, imaging, wearables, and other biomedical data sources to address patient burden.
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Experience with analytic methods and technologies (artificial intelligence, machine learning/ deep learning/ neural networks, natural language processing, visualizations) Bachelor's degree in Computer Science, Applied Mathematics/ Data Sciences/ Data Analytics or related discipline required, advanced degree preferred.
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Bachelor’s, Master’s or PhD in Electrical Engineering, Aerospace Engineering, Applied Mathematics, Physics, Radar Systems, or related technical field. You will collaborate with fellow researchers and engineers on multi-disciplinary teams spread across multiple STR campuses, groups, divisions, and with industry partners to interpret and understand customer objectives and develop innovative solutions to complex and challenging national security problems.
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Implementing and evolving our educational approach, which is rooted in social-emotional learning, STEAM (science, technology, engineering, art, and mathematics) curriculum, and nature-based learning.
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Analytical Skill, Analytical Thinking, Chemical Biology, Chemistry, Clinical Trials, Communication, Curiosity, Data, Data Analysis, Design of Experiments (DOE), Detail-Oriented, Drug Development, Drug Discovery Process, Ethics, Gene Therapy, Group Problem Solving, Immunology, Inventory Management, Laboratory, Large Group Presentations, Leading Project Teams, Life Science, Machine Learning (Ml), Medical Research {+ 9 more.
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Ph. D. in Bioengineering, Biochemistry, Biostatistics, Chemical Engineering, Computer Science, or similar discipline, with 0-5 years of experience and a strong focus on deep learning and/or cell engineering.
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7+ years of hands on experience in extractive metallurgy, mineral processing, hydrometallurgical process development with rare earth, copper, cobalt, nickel or actinides. Learning: Learning & Development Opportunities to grow your skills and career.
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machine learning applied mathematics on jobs Company: Birlasoft in Lexington, MA
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