Sort by
Refine Your Search
-
Listed
-
Country
-
Employer
- University of Sheffield
- ETH Zurich
- University of Glasgow
- Heriot Watt University
- Inria, the French national research institute for the digital sciences
- KTH Royal Institute of Technology
- NIMES UNIVERSITE
- Nature Careers
- Oak Ridge National Laboratory
- Universidad Complutense de Madrid
- University of Bristol
- 1 more »
- « less
-
Field
-
through the atmosphere. These models will be used, in Bayesian inference frameworks, to estimate surface fluxes from in situ and satellite observations. The derived emissions are used to track progress
-
atoms and construct designer lattices with targeted quantum or electronic properties. Extend AI-driven control approaches to thin-film synthesis platforms, such as pulsed laser deposition (PLD), by
-
description Third-cycle subject: Applied and computational mathematics The Department of Mathematics at KTH is announcing a PhD position in Mathematics with a specialization in AI, focusing on Bayesian inverse
-
the project. Main Tasks Extract genomic DNA from herbarium and fresh plant tissues using standard commercial kits (e.g. DNeasy Plant Mini Kit). Perform PCR amplification of target markers and prepare genomic
-
scarcity, regulatory gaps, and proliferation risk threatens to slow or even derail the commercialisation of fusion unless accompanied by targeted technical solutions and a strengthened non-proliferation
-
identify therapeutic targets. These efforts will generate large-scale, rich in vivo perturbation datasets, requiring scalable and reproducible pipelines for guide demultiplexing and assignment, cell-type
-
silico model of normal development. Bayesian inference will calibrate model parameters and highlight control points, with predictive accuracy benchmarked against existing perturbation datasets. O3. Map
-
Inria, the French national research institute for the digital sciences | Saint Martin, Midi Pyrenees | France | about 2 months ago
specifically, we use simulation-based inference (SBI) [1], a Bayesian approach that leverages deep generative models, such as conditional normalizing flows and score-diffusion models, to approximate
-
molecular data. 3. Identify when and why embryos fail using targeted computational perturbations. This inherently interdisciplinary project lies at the intersection of developmental biology, computational
-
, neurodegenerative, and inflammatory comorbidities. The chairholder will specifically focus on : 1/ Promoting precision psychopathology, developing more targeted prevention strategies, and proposing innovative