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This position is with the Signal Processing team at WHOOP. 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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Knowhow of Deep learning applications to Speech synthesis, LLM, and Speech-to-speech translations and hands-on experience on Speech Technologies like Speech Synthesis, voice cloning, etc.
$144,000 - $270,250 a yearFull-timeExpandApply NowActive JobUpdated Today - UpvoteDownvoteShare Job
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Data science, machine learning, optimization models, PhD 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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Employ advanced AI and machine learning models, including deep learning and predictive analytics, to identify novel materials and predict their performance within battery systems.
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Build and lead the data science team translating small molecule and proteomic drug discovery challenges into breakthroughs via deep learning applied to computational chemistry. This position is ideal for someone with deep expertise in machine learning, deep learning (particularly for building biologically meaningful representations of chemical structures), and computational chemistry, sciences and who is committed to building a high-preforming team.
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We are seeking a talented individual with expertise in generative AI, large language models and deep learning to elevate our AI initiatives for marketing and advertising. Expertise in large deep-learning model fine-tuning and deployment.
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The postdoctoral research associate will take part in challenging projects such as 1) building unsupervised learning models to identify patient subpopulations for precision medicine, 2) developing time-series deep learning and Transformer-based models for disease risk prediction and individualized treatment effect estimation from longitudinal patient trajectory, and 3) developing federated learning algorithms for causal inference across multiple institutions.
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Experience in core data science and predictive analytics methods: Statistics (t-tests, Poisson process), Segmentation and clustering techniques, predictive modeling: e.g., regression, classification, Time Series analysis: e.g., ARIMA, Traditional machine learning methods: e.g., Random Forest, ensemble model techniques, Optimization: e.g. linear programming.
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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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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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Experience leveraging complex data to drive business decisions, hands on experience in data science methodologies (predictive analytics, machine learning, patient level data triggers) using R, Pytong, Databricks and deep knowledge of Qlik, PowerBI, Tableau for visualization.
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Develop and maintain computational methods using deep learning for pathological image processing. With Three years of experience in scientific software development/analysis (specifically in Python, image processing and Deep Learning.
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Exceptional Expertise: PhD in any field plus 12 graduate-level credits in machine learning or artificial intelligence with an emphasis in neural networks & Minimum 2 years' experience designing, researching, or engaged in developing algorithms and software applications for neural networks in Python; solid knowledge of neural networks and deep learning algorithms and applications.
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PhD in applied mathematics, computer science, physics or other quantitative disciplines with a strong focus on machine learning and deep learning. Demonstrated mastery of a broad array of deep learning architectures with emphasis on transformer-based models and graph neural network architectures (transformer-VAEs, BERT, GPT, T5, Perceiver, GCN, GAT, MPNNs.
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Apply techniques such as classification, clustering, regression, NLP, deep learning (CNN, RNN, GANs), time series forecasting, Bayesian methods to build scalable solutions. Apply techniques such as classification, clustering, regression, NLP, deep learning (CNN, RNN, GANs), time series forecasting, Bayesian methods to build scalable solutions.
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