Sort by
Refine Your Search
-
Category
-
Program
-
Employer
-
Field
-
: 01/02/2026 Proportion of work : 100 % Workplace : ICube research unit, IGG team (University of Strasbourg) Desired level of education : PhD in computer science Experience required : PhD in
-
for Connected Industries), an international master’s program partially based on the local SYRIUS master’s program (Cloud Infrastructures & Distributed Systems). Teaching: the recruited candidate will teach
-
Offer Starting Date 1 Jan 2026 Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer
-
Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description The PhD will be carried out at LATMOS (Laboratoire Atmosphères, Milieux
-
6 Dec 2025 Job Information Organisation/Company CNRS Department Institut de Chimie de Nice Research Field Chemistry » Physical chemistry Chemistry » Computational chemistry Researcher Profile
-
Research FieldEnvironmental science » Global changeYears of Research ExperienceNone Additional Information Eligibility criteria • PhD in atmospheric sciences • Knowledge of cloud physics • Experience in
-
Eligibility criteria . PhD in atmospheric sciences • Knowledge of cloud physics • Experience in algorithm development and satellite remote sensing • Good written and spoken English • Ability to work
-
of dense cloud of cold atoms Supervision of PhD students Implementation of the missions on the Yb experiment Anderson Localisation ERC Andlica ANR LiLoA Where to apply Website https://emploi.cnrs.fr/Candidat
-
17 Oct 2025 Job Information Organisation/Company École nationale des ponts et chaussées Research Field Computer science Researcher Profile First Stage Researcher (R1) Positions PhD Positions Country
-
frequent cloud contamination. This scale mismatch prevents a coherent representation of radiative–thermal processes at the urban scale. This PhD will develop physics-informed deep learning models for data