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Scientist I Machine Learning for Generative Shape Modeling - Careers at Allen Institute Back To Openings Scientist I – Machine Learning for Generative Shape Modeling Department: CS - Computational Cell Science Start Your Application Scientist I – Machine Learning for Generative Shape Modeling.
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Experiences in data science and building and evaluating large scale machine learning & deep learning models. Data science, machine learning, optimization models, Master’s degree in Machine Learning, Computer Science, Information Technology, Operations Research, Statistics, Applied Mathematics, Econometrics, Successful completion of one or more assessments in Python, Spark, Scala, or R, Using open source frameworks (for example, scikit learn, tensorflow, torch.
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Strong understanding in the machine learning and data science technical ecosystems (e.g., Tensorflow, Pytorch, MLflow, Ray, LangChain, Data lake house (Databricks), Snowflake, SageMaker, Scikit-learn, etc.
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Bachelors/Masters degree in Statistics, Economics, Analytics, Mathematics, Computer Science, Information Technology or related field and 4 – 6 years' experience in an analytics related field, Data science, machine learning, optimization models, etc.
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Apply knowledge of statistics and mathematical theory to develop and integrate new and emerging technologies, such as machine learning and deep learning concepts and techniques. This position is focused on Data Science.
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Our researchers have a broad range of expertise related to computer science and electrical engineering, such as AI/ML, algorithms, digital signal processing, audio engineering, image processing, computer vision, data science & analytics, distributed systems, cloud, edge & mobile computing, computer networking, and IoT.
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This role demands a blend of expertise in data science and a deep understanding of the semiconductor industry, aiming to harness advanced data analytics, predictive modeling, and artificial intelligence to address complex business challenges.
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Responsible to analyze the trade / transaction monitoring alert from a trading platform and apply modern data science modeling techniques to predict future disputes & anomalies to proactively manage the risk.
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Areas of work include Artificial Intelligence, Computer Vision, Data Science, and Deep Learning. Areas of work include Sensing Hardware Engineering, Sensing ASIC Architecture, Algorithm Engineering, Machine Learning Engineering, Deep Learning, Firmware Engineering, Software Engineering, Quality Assurance Engineering, and User Studies and Human Factors Engineering.
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Key topics of interest include but are not limited to: natural language processing, large language models, predictive modeling for disease outcomes and personalized therapeutic recommendations, working with common data model EHR standards (e.g., OMOP), multi-modal (e.g., genomics, imaging, and clinical data) deep learning for clinical decision support, unsupervised learning to discover biologically-relevant disease subtypes, among others.
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Data science, machine learning, optimization models, PhD in Machine Learning, Computer Science, Information Technology, Operations Research, Statistics, Applied Mathematics, Econometrics, Publications or active peer reviewer in related journals or conference, Successful completion of one or more assessments in Python, Spark, Scala, or R, Using open source frameworks (for example, scikit learn, tensorflow, torch.
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Located in more than 30 offices and 10 creative studios around the world, its 6,000+ strong team combines strategy, technology, data science, and creative design with deep industry expertise and insights, to develop new digital solutions and business models of the future.
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Data Scientists contribute to projects involving one or more of the following disciplines: Data Science, Applied or Theoretical Machine Learning, Deep Learning, Deep Reinforcement Learning, Physics Inspired ML, Classification Systems, Natural Language Processing, Artificial Intelligence, or Computer Vision.
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Individuals in this role are expected to be recognized experts in areas such as artificial intelligence, machine learning, computational statistics, and applied mathematics, particularly including areas such as deep learning, graphical models, reinforcement learning, computer perception, natural language processing and data representation.
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Experience in data science, statistics, optimization, machine learning, and/or deep learning. Bachelor's degree in computational and engineering sciences (e.g., computer science, computational biology, electrical engineering, biomedical engineering) or other related fields.
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