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Lawrence Livermore National Laboratory's Center for Applied Scientific Computing is recruiting a Postdoctoral Research Staff Member with expertise in scientific machine...

mlscientist.com/postdoc-scie...

#scientificmachinelearning
September 30, 2026 at 6:06 PM
tenuretracker.bsky.social
Lawrence Berkeley National Laboratory (LBNL) is hiring:
Postdoctoral Researcher – Scientific Machine Learning & Computational Chemistry

#postdoc #computerscience #scientificmachinelearning
September 4, 2026 at 6:13 PM
juliahub.bsky.social
See how SciML combines physics and neural networks to capture missing physics, with vehicle ride and NVH case studies—and how the Dyad Agent accelerates the workflow. juliahub.com/white-papers...

#julialang #ScientificMachineLearning #SciML #PhysicsInformedAI #DigitalTwin #EngineeringAI
August 10, 2026 at 2:20 PM
juliahub.bsky.social
How do you uncover the physics your simulation is missing? See how Scientific Machine Learning combines vehicle test data with physical models to improve fidelity without creating a black box. juliahub.com/events/disco...

#julialang #ScientificMachineLearning #SciML #Automotive #EngineeringAI
August 5, 2026 at 4:14 PM
juliahub.bsky.social
JuliaHub is heading to JuliaCon Global 2026 in Mainz, August 10–15, with workshops and talks on SciML, engineering AI, modeling, compilers, security, and developer tools. juliahub.com/blog/juliahu...

#JuliaCon2026 #JuliaLang #JuliaHub #ScientificMachineLearning #EngineeringAI #ModelingAndSimulation
JuliaHub at JuliaCon Global 2026 - Blog | JuliaHub
Learn more about the impressive lineup of speakers and experts from JuliaHub at JuliaCon 2026.
juliahub.com
July 28, 2026 at 4:44 PM
tenuretracker.bsky.social
New York University is hiring:
Postdoctoral Researcher - NYU Courant

#postdoc #mathsky #scientificmachinelearning
July 15, 2026 at 5:43 AM
juliahub.bsky.social
Explore this recorded webinar to see how Scientific Machine Learning helps engineers combine test data with physics-based models to improve fidelity. juliahub.com/videos/disco...

#julialang #dyad #ScientificMachineLearning #VehicleDynamics #Simulation #Engineering #SciML
Discovering Missing Physics Through Scientific Machine Learning - Vehicle Ride Use Case - Video - JuliaHub
Learn how Scientific Machine Learning helps engineers uncover missing vehicle physics using test data while keeping simulation models robust and reliable.
juliahub.com
June 22, 2026 at 5:47 PM
juliahub.bsky.social
Learn how Scientific Machine Learning helps engineers combine test data with physics-based models to improve fidelity and uncover missing behaviour. juliahub.com/events/disco...

#julialang #Dyad #ScientificMachineLearning #VehicleDynamics #Simulation #Engineering
Discovering Missing Physics Through Scientific Machine Learning - Vehicle Ride Use Case - Event - JuliaHub
Explore how Dyad and Scientific Machine Learning help engineers uncover missing vehicle physics and improve ride model accuracy with test data.
juliahub.com
June 8, 2026 at 4:27 PM
structures-hd.bsky.social
STRUCTURES YRC members Manuel Klockow and Peter Lippmann presented their work at ICLR 2026 in Rio de Janeiro, Brazil. (1/5)

#STRUCTURESHeidelberg #ICLR2026 #MachineLearning #DeepLearning #GraphNeuralNetworks #earlycareerResearcher#HeidelbergUniversity #ScientificMachineLearning
STRUCTURES YRC members Manuel Klockow and Peter Lippmann presented their work “A Function-Centric Graph Neural Network Approach for Predicting Electron Densities” at the 14th International Conference on Learning Representations (ICLR) in Rio de Janeiro, Brazil. Their study introduces a graph neural network-based method, which is currently the most accurate known ML approach for electron density prediction on highly competitive benchmarks. We are pleased to see research supported by STRUCTURES represented at ICLR, one of the leading international conferences in machine learning and representation learning. Congratulations to Manuel Klockow, Peter Lippmann, and all collaborators involved. The International Conference on Learning Representations (ICLR) is one of the leading international conferences in artificial intelligence, with a particular focus on representation learning and deep learning. It brings together researchers from academia and industry to present cutting-edge work across machine learning, data science, and application areas such as computer vision, computational biology, natural language processing, robotics, and reinforcement learning.
May 29, 2026 at 12:34 PM