Sort by
Refine Your Search
-
Country
-
Employer
- Argonne
- Czech Technical University in Prague
- ICN2
- Inria, the French national research institute for the digital sciences
- Institut de Físiques d'Altes Energies (IFAE)
- Nature Careers
- Rice University
- Rutgers University
- The Peace Research Institute Oslo (PRIO)
- Umeå University
- Umeå universitet
- University of Washington
- Utrecht University
- Wageningen University & Research
- 4 more »
- « less
-
Field
-
colloidal routes, enabling precise control over size, morphology, composition, and structural complexity. This role offers an unparalleled opportunity to lead the computational core of a cutting-edge
-
single cells make decisions during differentiation, in particular during development. Building on Bonsai, a Bayesian framework that leverages tree structures for distortion-free exploratory analysis
-
model classifiers (PLS-DA, random forest, neural network, etc) towards unraveling materials structure-function relationships, and are familiar with optimization approaches such as genetic search, Bayesian
-
to study chemical transformations in materials. 2. Artificial Intelligence Applications: - Leveraging conventional machine learning techniques for materials property prediction and Bayesian approaches
-
the following criteria: a PhD in animal behaviour or a related field; demonstrable experience in conducting behavioural experiments and structural observations of animal social behaviour (preferably with primates
-
, IFAE is leading the construction of new baffles instrumented with photo sensors around the test masses. IFAE is actively participating in ET, coordinates the EU Horizon INFRA-DEV project for the ET
-
/bayesian/deep-learning analyses, with functional validation in spruce via CRISPR-Cas9 and nanoparticle delivery. The postdoc will join Professor Nathaniel R. Street’s team at UPSC, working closely with
-
, Micro-C/Hi-C, BS-Seq/EM-Seq), massively parallel enhancer assays (ATAC-STARR-seq), and comparative/bayesian/deep-learning analyses, with functional validation in spruce via CRISPR-Cas9 and nanoparticle
-
consideration market benchmarks, if and when appropriate, and internal equity to ensure fair compensation relative to the university’s broader compensation structure. We are committed to offering competitive and
-
imaging data. Building on recent critiques of overfitted prediction models, the project will implement a structured, multi-tiered analytical framework, comparing classical regression techniques with modern