Sort by
Refine Your Search
-
Listed
-
Employer
-
Field
-
machine-learning algorithms, and with lightning-fast Maxwell solvers for scattering simulations. You will not only work on the 3-D models in theory; you will also be trained in operating advanced microscopy
-
related discipline. A solid background in de novo protein design, protein structure prediction (Rosetta, AlphaFold, …), protein expression, structure elucidation, machine learning, C/C++ and/or Python with
-
Website https://www.academictransfer.com/en/jobs/358703/phd-in-scalable-safe-ai-for-sem… Requirements Specific Requirements A master’s degree AI, Machine Learning, Data Science, Computer Science or a
-
discipline. Experience with deep learning framework PyTorch or similar. Strong background in machine learning, image or signal processing. Knowledge of SotA models for multi-modality and scene understanding
-
Mathematics (Inverse Problems), Computer Science (Machine Learning, Computer Vision, Efficient Algorithms and High-Performance Computing), and Physics (Image Formation Modelling). Your project is part of
-
knowledge of and/or experience with validation of prediction models (regression or supervised machine learning), health technology assessment, decision curve analysis, and/or value-of-information analysis
-
-scale compound drivers. We will leverage machine learning methods to bridge the gap between drivers at coarse model resolutions and impacts captured by high-resolution observations. Job description Arctic
-
, silicon-proven AI/ML accelerator for transmitter error correction (digital predistortion/calibration). Your work will sit at the intersection of machine learning, DSP, and digital IC design, and you will
-
11 Apr 2026 Job Information Organisation/Company Delft University of Technology (TU Delft) Research Field Engineering » Computer engineering Engineering » Systems engineering Researcher Profile
-
You will join the “Professional Learning & Technology” (PLT) section of the Faculty of Behavioural, Management & Social Sciences. The PLT section specializes in research on professional learning in and