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PhD in computer science, applied mathematics, physics, computational biology or other quantitative disciplines. 5+ years of experience in developing deep learning and/or NLP models.
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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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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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PhD in Biochemistry, Analytical Chemistry or related scientific discipline and 2+ years of hands-on experience or BSc/MSc with 7+ years of relevant industry experience is required. Strong hands-on experience working with analytical protein characterization techniques (UV, SDS-PAGE, cIEF, etc.
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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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Responsibilities: Technical Leadership: Lead by example, contributing hands-on expertise in Python, Airflow, Snowflake, and AWS to develop scalable and efficient solutions. Job Title: Hands-On Engineering Manager Location: Newton, MA About Us: My client is a rapidly growing eCommerce beverage distribution company that achieved $200 million in revenue last They are at the forefront of revolutionizing the beverage industry by providing exceptional service and innovative solutions.
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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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Hands-on experience in handling, processing, integrating, and analyzing large heterogenous biological data sets related to industrial drug discovery research (e.g., sc/snRNAseq, ATAC-seq, genomics, proteomics, etc.
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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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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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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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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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Expertise and hands-on experience in operating and maintaining analytical instruments including but not limited to GC, FTIR, UV-Vis, DSC, TGA, SEM, XPS, Raman, IC, 1H-NMR, and 19F-NMR. SES has an opening for an Analytical Scientist, who is passionate about mobile energy storage and is an expert on Material characterization.
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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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learning applied mathematics hands on jobs Company: Birlasoft in Lexington, MA
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