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Methods vary widely but include applied mathematics, deep learning, machine learning, probability modeling, Bayesian reasoning, quantum computing, spatial statistics, spatio-temporal modeling, decision support, and data mining.
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Programming Skills: Proficiency in programming languages like Python, R, or similar, along with experience in using machine learning libraries such as TensorFlow, PyTorch, or scikit-learn.
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Conduct experiments to train, tune, and optimize Machine Learning / Deep Learning models for delivery onto client devices. As an Embedded Engineering Technologist, on the CSG CTO Advanced Architectures team, you'll gain experience as an embedded software engineer and have the opportunity to gain strong data science skills to develop and deliver enterprise-grade Artificial Intelligence and Machine Learning solutions across the client devices portfolio.
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Contribute to the development and refinement of machine learning models, including deep learning models for computer vision and natural language processing, to enhance Cria's capabilities in neonatal medicine.
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In-depth knowledge of deep learning learning algorithms especially for NLP and LLM. I have been working at Nutanix since May 2021, primarily on the machine learning algorithm side but also in areas like MLOPs lifecycle management, data preparation, and deployment.
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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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Familiarity with machine learning and deep learning frameworks (e.g., TensorFlow, PyTorch) for analyzing Earth observation data in the context of carbon monitoring and climate analysis is advantageous.
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Hands-on experience of machine learning methodologies including advanced analytics tools (such as R and Python) along with applied mathematics, ML and Deep Learning frameworks and libraries (TensorFlow, PyTorch, Keras) and ML techniques.
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Are you a passionate Machine Learning Engineer with a strong background in SageMaker, prompt engineering, and LLM (Large Language Model) model tuning? If you are an ambitious Machine Learning Engineer with a proven track record in SageMaker, prompt engineering, and LLM model tuning, we would love to hear from you.
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Automate data collection and processing algorithms, including the generation of Machine Learning/Deep Learning models for automatic data labeling and anomaly detection. Proficient in data-driven methods, Regression, Neural Networks, Machine Learning and Deep Learning.
$139,100 - $185,400 a yearFull-timeExpandApply NowActive JobUpdated Today - UpvoteDownvoteShare Job
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Relevant Keywords: Large Language Models, Machine Learning, Python, Deep Learning, Gemini, GPT, GPT-3, GPT-4, Synthetic Data, ML Fundamentals, Pytorch, TensorFlow, Data Engineering, Data Preprocessing.
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You will collaborate with company advisors from MIT and Harvard whose expertise span machine learning, medical imaging, and getting products successfully through FDA. We are looking for an individual with an exceptional Machine Learning background and track record of proven delivery who is not shy to take on extensive responsibilities.
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Fundamental knowledge of and/or experience developing and applying algorithms in one or more of the following Machine Learning areas/tasks: robust deep learning, efficient machine learning, active learning, multimodal learning, and large language models.
$108,600 - $126,720 a yearFull-timeExpandApply NowActive JobUpdated Yesterday - UpvoteDownvoteShare Job
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Experience with building machine/deep learning models with at least one common framework such as PyTorch, Tensorflow, or Keras. Strong understanding of applied machine learning and deep learning topics.
$160,000 - $280,000 a yearExpandApply NowActive JobUpdated 6 days ago - UpvoteDownvoteShare Job
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The incumbent will apply advanced data analysis skills and machine learning in collaboration with a group of computational and biological scientists to perform translational oncology research around multimodal translational data sets for programs in clinical development.
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