Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Program
-
Field
-
. The project is conducted in collaboration with the deep probabilistic programming group of Thomas Hamelryck : https://di.ku.dk/english/research/groups/machine-learning-in-biology/?pure=en/persons
-
Modeling and Simulation: Utilizing an in-house simulation code (Python/GPU) based on the Discrete Element Method (DEM). Contributing to model development: integrating new physical building blocks
-
( ABD ) status will also be considered (Degree Conferral in Process) Certifications/Licenses Required Knowledge, Skills, and Abilities Experience in GPU programming Experience working in interdisciplinary
-
computing or AI engineering, with hands-on experience delivering and supporting production software, data-intensive workflows or computing services Strong programming skills and practical experience with
-
infrastructure for deep learning, speech, and audio research, including Aalto University’s large-scale scientific computing cluster with CPU and GPU nodes, access to CSC’s national computing infrastructure
-
junior researchers All applicants should have: • A solid foundation in machine learning, deep learning, and computer vision • Strong skills in applied mathematics, probability, and programming, such
-
quantification, time-series modelling, or multimodal data analysis. Excellent programming skills in Python and experience with deep-learning frameworks (preferably PyTorch). Experience developing
-
team and supported by cutting-edge HPC and GPU infrastructure, you will contribute to internationally leading research, publish in high-impact journals and present your work at major scientific
-
HPC and GPU infrastructure, you will contribute to internationally leading research, publish in high-impact journals and present your work at major scientific conferences. About You You will be
-
and high-performance or GPU-accelerated computing environments. Applying rigorous methods for external validation, transportability, subgroup performance, fairness, uncertainty quantification, and