Bayesian Networks Jobs (NOW HIRING)

... neural networks (DNN), recurrent neural networks (RNN), attention-based transformers, etc ... Bayesian analysis, and covariance matrices. * Proficiency in agentic systems that can automatically ...

... as deep neural networks, recurrent neural networks, and attention‑based transformers ... non‑parametric probability distributions, Bayesian analysis, and covariance matrices.

... Argo Workflows, Jenkins, Django, Bayesian machine learning methods, Git and GitHub and ... networks for chemistry; applied knowledge of Python to write data preprocessing scripts, train ...

Bayesian models and deep neural networks), optimization methods, and other ML techniques to different applications in business and engineering. Routinely build and deploy ML models on available data.

... networks, space, time, or causal inference based on discrete and dependent data, either Bayesian or non-Bayesian. If desired, the postdoctoral scholar may teach one course in the Department of ...

... networks, space, time, or causal inference based on discrete and dependent data, either Bayesian or non-Bayesian. If desired, the postdoctoral scholar may teach one course in the Department of ...

Our aim is to develop and apply a rigorous theory of latent internal structure in neural networks ... Bayesian belief updating over hidden states of a world model. Even when trained on simple token ...

... Bayesian, coordinate descent, gradient descent, and evolutionary * Utilizes big data computation and storage models to create prototypes and data sets * Develops Convolutional Neural Networks

... networks), emergent behavior (e.g. flocking, swarming); optimization theory and numerical optimization with applications to data science; high-dimensional statistics; Bayesian statistics; resampling ...

Graph neural networks, geometric deep learning, equivariant models, neural operators, tensor ... Probabilistic inference, Bayesian modeling, variational inference, Monte Carlo methods, simulation ...

... Networks, SVMs, Random Forests, Gaussian Processes, and other techniques. * Knowledge of common ... Bayesian Statistics. * Proficient C/C++. * Big Data technologies such as Hadoop MapReduce, HDFS ...

... as Bayesian, coordinate descent, gradient descent, and evolutionary * Utilizes big data computation and storage models to create prototypes and data sets * Develop Convolutional Neural Networks and ...

... networks (e.g., YOLO, CenterNet) and modern image classification techniques • Software expertise ... Bayesian models, etc. • B.S., preferably M.S. or Ph.D in engineering, math, computer science, or ...

... Bayesian analysis, and others to develop and evaluate algorithms that improve product/system ... networks. * Experience with one or more common statistical tools such as SAS, R, KNIME, Matlab.

Graph Neural Networks (GNNs) for geometry/topology-aware modeling * Transformers for sequential and ... Bayesian, gradient-based, and gradient-free optimization * Reinforcement Learning (PPO, DDPG, SAC ...

... networks for imaging, transformers, or temporal models for signals) and apply self-supervised and representation learning under limited labels. * Statistical depth. Apply survival analysis, Bayesian ...

... networks for imaging, transformers, or temporal models for signals) and apply self-supervised and representation learning under limited labels. * Statistical depth. Apply survival analysis, Bayesian ...

Deep Learning & Neural Networks : CNNs, RNNs/LSTMs, Transformers, attention mechanisms * Statistical modeling & algorithms : optimization, Bayesian inference, probabilistic modeling * Proficiency in ...

The company's products are powered by Coherent Distributed Networks (CDN), empowering warfighters ... Apply advanced statistical methods (such as regression modeling, Bayesian inference, surrogate ...

... networks for imaging, transformers, or temporal models for signals) and apply self-supervised and representation learning under limited labels. * Statistical depth. Apply survival analysis, Bayesian ...