Sort by
Refine Your Search
-
Listed
-
Country
-
Field
-
(cover letter) CV Academic Diplomas (MSc/PhD – in English) List of publications A mandatory research statement (max. 2 pages) describing your understanding and achievements in optical computing for neural
-
11 Dec 2025 Job Information Organisation/Company NEW YORK UNIVERSITY ABU DHABI Research Field Computer science Engineering Mathematics Researcher Profile Recognised Researcher (R2) Established
-
Max Planck Institute for Evolutionary Biology, Plön | Plon, Schleswig Holstein | Germany | 22 days ago
theoretical models and computer simulations. Adaptation of complex traits is assumed to occur through subtle frequency changes at many loci following a shift in the trait optimum, i.e. polygenic adaptation
-
compute continuum for 6G RAN open architectures. The Advanced Networking Lab is a member of the Center for Wireless Technology Eindhoven (CWTe) which is part of the Department of Electrical Engineering
-
requirements: To apply for the Post-Doctoral Research Grant, applicants must comply with the following requirements, for which documentary evidence must be provided: PhD in Architecture or Civil Engineering
-
United Kingdom Application Deadline 1 Jan 2026 - 00:00 (UTC) Type of Contract Other Job Status Full-time Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job
-
Profile Recognised Researcher (R2) Country Sweden Application Deadline 12 Jan 2026 - 22:59 (UTC) Type of Contract Temporary Job Status Full-time Is the job funded through the EU Research Framework Programme
-
and setup: Within the project, we follow a multidisciplinary collaborative approach for which we are have recruited 3 PhD students focusing on material science, advanced in vivo imaging and computation
-
The postdoc will join a world-leading research environment at the Chalmers Security & Privacy Lab as a member of the CryptoTeam . The Crypto Team currently has two faculty members and four PhD students
-
techniques—including vision-language architectures (e.g., CLIP, BLIP), fine-tuning large language models for clinical NLP, and self-supervised contrastive learning—the models will learn to effectively combine