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Strong understanding of data science approaches including machine and deep learning; background in immunology, biology or oncology a plus. Demonstrated experience applying machine learning and deep learning algorithms to medical imaging analytics including CT, MR, and/or PET scans; experience in additional imaging domain (H&Es, IHC) a plus.
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Apply classical and machine learning methods to derive insights from integrating and analyzing clinical response, tumor transcriptomic/genetic data, and radiographic image features.
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Recent or soon-to-be completed Ph. D. (typically completed within 3 years) in Computer Science or Mathematics with strong background in one or more of the following: Statistical machine learning, Bayesian deep learning, probabilistic and differentiable programming, probability and measure theory.
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Experience in machine learning methods (e.g. multivariate regression, feature engineering, random forests, XGBoost, elastic nets, hierarchical bayesian regression, unsupervised learning, clustering/segmentation)Total BenefitsHere are just a few of the benefits you’d enjoy working in this role for Kimberly-Clark.
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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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PhD in Computer Science, Applied Mathematics, Computational Biology, Statistics, Biostatistics or similar field; or equivalent combination of degree and experience. Principal Scientist - Computational Biologist/ML Researcher - OCTO.
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Collaborate with the biosciences team on the targeted acquisition of experimental data to improve our machine learning models. Ph. D., M.Sc., or M.Eng. in Computer Science, Physics, Applied Mathematics, Materials Science, Computational Biology, or related field.
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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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Design and implement deep-learning (DL) and machine-learning (ML) models to extract valuable insights from large repositories of time-series/biosensor data. As a Signal Processing Engineer, focused on Deep Learning, you will be part of a cross-functional team composed of Signal Processing, WHOOP Labs, Firmware, and Data Science.
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In this role specifical, the Principal Data Scientist (Deep Learning) will lead algorithmic solution design, rapid prototyping, and technical review for the ML/AI models underlying personalized user experiences and content promotion.
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The scientist must have a broad knowledge of modern data science methods with an emphasis on machine learning to advance multi-objective molecular design efforts. This is an exciting opportunity as we combine the best of physics-based modeling with data analytics and machine learning to accelerate drug discovery and bring benefit to patients.
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Experience in structural health monitoring (SHM), non-destructive evaluation (NDE), and non-invasive testing,+ Experience in electrical measurements, building of complex experimental setups, and sensor development,+ Experience in advanced signal processing, including machine learning,+ Proficiency in software development using Python, MATLAB, and LabVIEW,+ Experience in modeling & simulation studies using COMSOL,+ Ability to plan and organize assignments to meet project deliverables.
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The Data Scientist is responsible for using advanced statistical, algorithmic, machine learning, data mining and visualization techniques to help advance and complete client projects.
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5+ years of work-related experience in Deep Learning and Machine Learning, including deep learning frameworks TensorFlow, PyTorch and LangChain with GPU and CUDA experience being extremely advantageous.
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As a member of the Data Engineering team, the Machine Learning Engineer will work closely with Business domain experts and Data Scientists to solve real-world oil and gas midstream problems using advanced analytics, machine learning, and artificial intelligence.
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