Sort by
Refine Your Search
-
Department of Statistics (DE-UFSCar) and the University of São Paulo's São Carlos Institute of Mathematical and Computer Sciences (ICMC-USP). The fellow will be based at UFSCar. The position is open to
-
and radar remote sensing, climate time series, and hydrological models. The work will employ machine learning and explainable AI techniques to improve flood prediction under different hydroclimatic
-
must hold a PhD in astronomy/astrophysics (awarded within the last 7 years), with experience in stellar astrophysics, survey data analysis, or machine learning, and strong programming skills
-
position within a Research Infrastructure? No Offer Description The research proposal aims to use machine learning, including large-scale language models, to analyze large datasets of smaller Solar System
-
the São Paulo Research Foundation through its Young Investigator Grant modality, invites applications for a post-doctoral position. The project is based at the Institute of Mathematics, Statistics and
-
knowledge on signal processing, statistical analysis and computer programming skills. Deadline: 10/31/2025 Furthermore, applicants must send the following documents via email to Alberto Luiz Serpa at alserpa
-
collisions applied to Large Hadron Collider (LHC) and/or the Electron-Ion Collider (EIC) and experimental data analysis with ALICE (A Large Ion Collider Experiment) or Stellar Intensity Interferometry (SII) in
-
(HR+/HER2-) and aims to develop predictive models of therapeutic response using machine learning combined with Fourier-Transform Infrared Spectroscopy (FTIR) applied to blood, saliva, and tumor tissue
-
. The successful candidate must have: 1) Experience in the field of instrumental neutron activation analysis; 2) Experience in analytical techniques such as inductively coupled plasma optical emission spectrometry
-
(XRD) for structural analysis; • Dynamic light scattering (DLS) for particle size distribution and colloidal stability; • Magnetometry techniques (e.g., VSM, SQUID) for magnetic property evaluation