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About the role This project will develop new machine learning techniques to enable efficient production of bespoke products, such as medicines in a high-volume, high-variation manufacturing setting
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Do you have a strong technical background in Machine Learning and Numerical Modelling? Are you interested in working with industry to develop Machine Learning methodologies and protocols needed
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) to develop accelerated AI, machine learning, and robotics algorithms with a strong focus on computational efficiency, memory reduction, and energy-aware deployment. The role targets foundation models
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and Machine Learning Researcher will report to Ayan Paul, Research Scientist at EAI. There will also be opportunities to work on industry collaborations. Responsibilities will include building an ETL
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that learn nonlinear cross-fidelity correlations. More broadly, scientific machine learning methods such as physics-informed neural networks (PINNs) and operator learning (DeepONet, Fourier Neural Operator
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performance, yet their atomic-scale origin and role in reactivity remain poorly understood. The project addresses this open problem by integrating high-throughput Density Functional Theory, machine-learning
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Department of War security clearance. Knowledge, Skills, and Abilities: Deep Technical Knowledge: You have performed extensive research or engineering activities in applied machine learning and artificial
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a machine learning model (foundational model) to propose protocols of sequential induction of transcription factors to generate desired cell subtypes. The project will be conducted in close
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Center for Emergence in the Molecular and Cellular Sciences (NCEMS) and the Institute for Computational and Data Sciences (ICDS) at Penn State seeks an outstanding scientist to fill a Machine Learning
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based on machine learning tools for energy problems related to prediction. The application domains include both industry and climate changes. The first two months will be devoted to the study of