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Master's Degree in related field (e.g., Data Science, Predictive Analytics, Machine Learning, Statistics, Applied Mathematics, Computer Science) Ability to design and implement end-to-end machine learning pipelines for data ingestion, processing, modeling, and deployment.
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The machine learning engineer will be responsible for data from the electronic data repository, including EEG, video, and peripheral blood biomarkers. The machine learning engineer will work and mentor a team of researchers in searching for patterns hidden in large data sets for research in neurology.
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PhD in a scientific discipline and a minimum of 5 years related experience; may include post-doctoral experience; Masters and 7 years of related experience; Bachelors and 9 years of related experience in data engineering, systems engineering, machine learning and operations, MLOps, or similar roles; or equivalent industry experience.
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4+ years of experience implementing and applying algorithms for machine learning and artificial intelligence. Proficiency with standard machine learning tools and frameworks: Python, PyTorch, Huggingface Transformers.
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We are seeking a highly motivated machine learning and AI Principal Scientist to join our Computational Tissue Imaging Research team in the Translational Bioinformatics organization. Strong coding skills in Python, and experience with machine-learning and deep learning libraries (e.g., Scikit-learn, TensorFlow, PyTorch, etc.
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As a Machine Learning Engineer, you’ll develop ML-based metrics for evaluating an autonomous vehicle system offline at large scale. Machine Learning development experience.
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Hands on experience working in an infrastructure managed services environment, supporting complex engineered solution in production with Artificial Intelligence/Machine Learning/High Performance Compute Systems and Platforms, Converged/ Hyper-Converged infrastructure along with fluency in AI/ML pipelines, Nvidia GPU optimization, InfiniBand networking, Machine Learning operating systems such as cnvrg.io, Compute Orchestration Platform such as runai etc.
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Master's degree (or higher) (or foreign education equivalent) in Engineering, Statistics, Data Science or a related field plus one (1) year of experience as a Data Analyst or a related occupation performing advanced analytics using predictive modeling and machine learning algorithms.
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5+ years of experience in machine learning engineering, data science, or a related field. Knowledge of machine learning algorithms, statistical modeling, and data structures.
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Apply machine-learning methods (e.g. deep learning, reinforcement learning, and generative adversarial networks) to biological imaging data to evaluate temporal and spatial effects of novel targets.
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Strong familiarity with topics of protein and macromolecular structures and interactions with other proteinsExperience with cloud environments such as AWSExperience in developing machine learning platforms using modern ML frameworks for deep learning (, tensorflow, keras, MXNet) & deploying in services such as Amazon Sagemaker.
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PostEra is using machine learning, applied to medicinal chemistry, in order to expedite the discovery of new drugs and get new cures to patients faster. PostEra offers medicinal chemistry powered by machine learning.
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Collaborate effectively with renowned GSK experts in genetics, genomics, computational bio & chem, structural biology and medicinal chemistry, artificial intelligence / machine learning, and digital technologies to build the datasets, tools, models, and infrastructure necessary for oligo design & development.
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One or more of industry applicable security certifications, CCNA, CYSA+, Sec+, CISM, NSE, CISMStrong technical expertise required in one or more of the following areas: Firewalls, Network Intrusion Detection and Prevention Systems, Routers and Switches, MDM, EDR, SIEM, AI, Machine Learning, Packet decoding and analysis, Web Proxy Servers, Load Balancing, SSL VPN Solutions, Web application firewalls and data loss prevention technologies.
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Experience deploying machine learning models on local data center, cloud, and embedded systems. Experience with training and fine-tuning LLMs for different dialog system tasks using PyTorch Deep Learning frameworks and performing data wrangling and tokenization.
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