Top Tweets for #ScientificMachineLearning
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
๐ We're excited to welcome Dr. Rohit K.S.S. Vuppala (Oklahoma State University) as a Committee Member of #IOCUS2026 (20โ22 May)!
๐ก Register FREE by 18 May: https://t.co/QGYjNaYFmF
More info: https://t.co/62naM98QIj
#UrbanAirMobility #ScientificMachineLearning
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
Solved inverse reaction-diffusion with PINN! Target ฮฑ=0.1 & k=0.5 โ inferred 0.09951 & 0.49937 under 3% noise Multi-param, no Bayesian, 186s on CUDA #PINN #PhysicsInformedNeuralNetworks #ScientificMachineLearning #DeepLearning #AIforScience
Researchers at @UTAustin Introduce #Panda: A Foundation Model for Nonlinear Dynamics Pretrained on 20,000 Chaotic ODE Discovered via Evolutionary Search
#scientificMachineLearning #SciML #MachinelearningDynamicalSystems #MLDS
https://t.co/7V4FHBzE2s
Looking forward to feedback and discussions!
#DeepLearning #ScientificMachineLearning #StochasticProcesses #BSVIE #QuantitativeFinance #StochasticControl #Preprint #MathAI #Research
Very happy to be in the Kalandra University Camp in #Chalkidiki, #Greece to give a plenary talk on #ScientificMachineLearning for #ComplexSystems at the 30th Summer School " #Dynamicalsystems & #Complexity". Congratulations to the organizers! https://t.co/yi0IHxYZrj
How #ScientificMachineLearning is Revolutionizing #Research & #Discovery
#SciML #Scientific #MachineLearning #ML
https://t.co/rBIhQ9GCd6
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
A robot that autonomously checks industrial systems is in the making! Judith Dijk from TNO gave us a peek in its brain at the symposium on the Applications of Scientific Machine Learning organized by CWI. https://t.co/GuXmXiAZZJ #scientificmachinelearning
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
Neural PDE Surrogates that robustly solve Parametric Partial Differential Equations (#PDEs) is still largely an unsolved problem in #ScientificMachineLearning (#SciML). The CAPE model (https://t.co/sixfGxq8wo) is a first step towards solving this challenging problem. #neuralPDEs
JUST IN: #YoungjoonHong appointed as Associate Professor at KAIST, specializing in #Mathematics & #ScientificComputing with focus on #GenerativeModels for #ComputationalMaterials & #ScientificMachineLearning. #Sungkyunkwan University spearheads research in #AI,...
Doing Small Network Scientific Machine Learning In #Julia 5x Faster Than #PyTorch https://t.co/2ws3R8jlFQ #scientificmachinelearning #tensorflow #sciml #fluxjl
Doing Small Network Scientific Machine Learning In #Julia 5x Faster Than #PyTorch https://t.co/LLRNs1agjp #scientificmachinelearning #tensorflow #sciml #fluxjl
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
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