Top Tweets for #ScientificMachineLearning
@MIT_CSAIL A research map of selected publications across my PhD study:
#GenerativeAI #AIforScience #ComputationalDesign #ScientificMachineLearning #PhDDefense #worldmodel
๐Fully Funded PhD in Scientific Machine Learning: Toward Scientific Foundation Models (Netherlands ๐ณ๐ฑ)
๐ถ Fully funded 4-year PhD with a salary ranging from โฌ3,059 โ โฌ3,881/month + excellent benefits
โ
Interested in #ScientificMachineLearning #FoundationModels #PhysicsInformedAI ๐คโก๐
โ
Highly recommend this interdisciplinary #PhDPositionfor up to 4๏ธโฃ years within TU Delft | Electrical Engineering, Mathematics and Computer Science @tudelft ๐ณ๐ฑ
๐ This #phdprojectis titled โ๐ฆ๐ฐ๐ถ๐ฒ๐ป๐๐ถ๐ณ๐ถ๐ฐ ๐ ๐ฎ๐ฐ๐ต๐ถ๐ป๐ฒ ๐๐ฒ๐ฎ๐ฟ๐ป๐ถ๐ป๐ด, ๐ง๐ผ๐๐ฎ๐ฟ๐ฑ ๐ฆ๐ฐ๐ถ๐ฒ๐ป๐๐ถ๐ณ๐ถ๐ฐ ๐๐ผ๐๐ป๐ฑ๐ฎ๐๐ถ๐ผ๐ป ๐ ๐ผ๐ฑ๐ฒ๐น๐โ
Youโll work on:
๐ท Developing scientific machine learning methods combining AI with physical laws and differential equations
๐ท Exploring physics-informed neural networks, neural operators, and hybrid physics-ML approaches
๐ท Building scientific foundation models for inverse problems and scientific discovery
๐ท Investigating uncertainty-aware AI methods for reliable scientific predictions
๐ท Studying generalization across physical systems, geometries, and boundary conditions
๐ท Applying AI to domains such as climate science, geoscience, renewable energy, and power grids
๐ Contribute to advancing next-generation AI systems for science and engineering while helping bridge machine learning, physics, and real-world scientific discovery.
โ
Work with Dr. Jing Sun within the Faculty of Electrical Engineering, Mathematics & Computer Science at TU Delft
โฐ ๐๐ฒ๐ฎ๐ฑ๐น๐ถ๐ป๐ฒ: ๐ญ๐ฐ๐๐ต ๐๐๐ป๐ฒ, ๐ฎ๐ฌ๐ฎ๐ฒ
๐ Full details & apply here:
๐https://t.co/9wlkoO6S5Z
๐ฉ Want more like this?
โ Follow @PhdScanner and join WhatsApp for updates:
https://t.co/d89Rl8tH7S
๐ Visit: https://t.co/yET6DsYARx
#fullyfundedPhD #PhDposition #TUdelft #Netherlands #ScientificMachineLearning #FoundationModels #PhysicsInformedNeuralNetworks #AIforScience #MachineLearning #DeepLearning #ResearchOpportunity
@phdhardtalk
โป๏ธ Share with someone applying this cycle
This is a great example of how combining physics, engineering insight, and scientific machine learning can deliver real operational impact in complex infrastructure systems.
#JuliaLang #ScientificMachineLearning #SciML #DigitalTwins #PredictiveMaintenance #AssetHealth #WaterIndustry #JuliaHub
https://t.co/PDgUHm5unb
Over 60 participants from 26 universities in 6 countries attended the workshop on #ScientificMachineLearning last week! This concluded a research semester program on the emerging field of #SciML. ๐https://t.co/36Be71sisV
๐ฃ 2nd workshop on Physics Enhancing ML in Applied Mechanics, 20/11/23, London & online @iop_conferences: program is out!
๐ฏ Free registration by 14/11
info: https://t.co/f9WTifg7cx
๐ reshare
#phiML #scientificmachinelearning #physicsinformedMachineLearning #explainableML
๐ฃ 2nd workshop on Physics Enhancing Machine Learning in Applied Mechanics, 20 November 2023, London @iop_conferences
๐ฏ Free registration, hybrid event
info: https://t.co/75UpW84uN9
๐ reshare
#phiML #scientificmachinelearning #physicsinformedMachineLearning #explainableML
We will host this talk tomorrow at 15:00 (CEST) / 09:00 (ET) / 06:00 (PT). Please feel free to join us if you're interested in #PDE solving using #NeuralNetworks, #ScientificMachineLearning, or #SciML #Simulations in general. ๐ฌ๏ธ๐ซง
๐ข#AI4Science Talk on 28.08 at 15:00 (CEST) / 09:00 (EDT) on โPDE-Refiner: Achieving Accurate Long Rollouts with Neural PDE Solversโ by @phillip_lippe from MSR/UvA.
Please join us on Zoom if you're interested!
Details: https://t.co/xE6NUTJgVX
#ML4science #CFD #PDEs #AI4Science
๐ขWe are #hiring PostDocs!๐ข
Looking for talented candidates with backgrounds either in #ComputationalSciences or in #ScientificMachineLearning. Experience in #Biomechanics is appreciated.
@mox_lab @polimi
@UniTrento
2 years funding from #PRIN @mur_gov_
RTs appreciated๐
Scalable algorithms for physics-informed neural and graph networks
Khemraj Shukla, Mengjia Xu, Nathaniel Trask & George E. Karniadakis
โ https://t.co/joBLPuKZNC
#GraphNeuralNetworks #PINNs #Scalability #ScientificMachineLearning #MachineLearning #GraphNetworks #Algorithms #ML
The session on #ScientificMachineLearning is not to be missed!
https://t.co/GBJ8YezLXl
La session sur l' #ApprentissageAutomatiqueScientifique est ร ne pas manquer !
https://t.co/e1AFIeq1u9
#2022cmssummermeeting #mathematics #mathisfun #summer #math #conference #newfoundland
Watch this video to see how some of the latest advances in scientific machine learning have been made possible through the language-wide differentiable programming techniques offered by the Julia package ecosystem.
https://t.co/0yiczP9vpm
#julialang #ML #ScientificMachineLearning
Karen Willcox from #OdenInstitute penned this opinion piece for @AeroTestingMag on the potential of #scientificmachinelearning in our machine-driven age. #AI #SML #MachineLearning https://t.co/H8CutQxpN2
Scientific Machine Learning Enables Potential to Design at โNear-Interactive Speedsโ
https://t.co/o5KJ7V4ssz
#HPC #MachineLearning #3D #ScientificMachineLearning @ARPAE @OdenInstitute
Here's yesterday's SFI colloquium from Science Board Member Karen Willcox of @OdenInstitute on how to bring the power of #MachineLearning together with the #rigor of model-order reduction and the structure of #physics:
https://t.co/vkbOtZyhCn
#MOR #ML #ScientificMachineLearning
Follow this thread for highlights from today's colloquium by SFI External Prof Karen Willcox of @OdenInstitute at @UTAustin presenting on "Scientific #MachineLearning: Where #Physics-based Modeling Meets #Data-driven Learning"
Also streaming to Facebook:
https://t.co/MNtYkdzEoK
"It's not rocket science" may be a clichรฉ but it's still as complicated as ever. Professor Karen Willcox @OdenInstitute leads research that uses #scientificmachinelearning to deliver faster & cheaper #rocket engine designs for @usairforce https://t.co/l4jBKbTtiN @CockrellSchool
"It's not rocket science" may be a clichรฉ but it's still as complicated as ever. #OdenInstitute researchers @UTAustin use #scientificmachinelearning to deliver faster and cheaper rocket engine designs for @usairforce https://t.co/rTj6Ae2hQE
Sitting in on @ChrisRackauckas class on #ScientificMachineLearning (18.337) at @MIT.
He's a great lecturer (but he does make me think of the MIT joke about drinking from a ๐ฅhydrant!)
The serial performance parts were quite good, and the discussion of @JuliaLanguage
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