Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Employer
- Susquehanna International Group
- Technical University of Denmark
- University of Southern Denmark
- Curtin University
- Humboldt-Stiftung Foundation
- University of Groningen
- DAAD
- Nature Careers
- University of Copenhagen
- ;
- AALTO UNIVERSITY
- Carnegie Mellon University
- Cornell University
- NTNU - Norwegian University of Science and Technology
- Technical University of Munich
- Universite de Moncton
- ; Brunel University London
- ; University of Reading
- Aarhus University
- Ariel University
- CWI
- Delft University of Technology (TU Delft)
- Delft University of Technology (TU Delft); Delft
- European Magnetism Association EMA
- ISCTE - Instituto Universitário de Lisboa
- Imperial College London
- Leibniz
- Leiden University
- Max Planck Institute for Human Cognitive and Brain Sciences •
- Murdoch University
- NORCE
- National Institute for Bioprocessing Research and Training (NIBRT)
- National Institute of Chemistry Slovenia
- Norwegian Meteorological Institute
- Norwegian University of Life Sciences (NMBU)
- Queensland University of Technology
- SciLifeLab
- Swedish University of Agricultural Sciences
- Technical University Of Denmark
- UNIVERSITY OF HELSINKI
- University of Cambridge;
- University of Luxembourg
- University of Minnesota
- University of Oslo
- University of Southern Queensland
- University of Twente
- University of Vienna
- VIB
- Østfold University College
- 39 more »
- « less
-
Field
-
modelling of materials and machine learning. Experience in atomistic modelling (molecular dynamics, density functional theory) and machine learning is important, as well as a strong interest in pursuing
-
described in the project overview. Owing to the current composition of the project team, there will be a mild preference for candidates opting for project 2 on “Models and machine learning”. An explanation
-
, memristive devices), and the evaluation with e.g. machine learning and image processing benchmarks Requirements: excellent university degree (master or comparable) in computer engineering or electrical
-
background in CMOS/VLSI design, computer architectures (preferred RISC-V architecture), and deep learning principles. Experience with industry-standard EDA tools such as Cadence suite: Genus, Virtuoso, Spectre
-
One Research Associate position exists in the data-driven mechanics Laboratory at the Department of Engineering. The role is to set up a machine learning framework to predict the plastic behaviour
-
for the modeling and simulation of 3D reconfigurable architectures e.g. based on emerging technologies (e.g. RFETs, memristive devices), and the evaluation with e.g. machine learning and image processing benchmarks
-
biodiversity or occurrence data (e.g., GBIF). Understanding of species distribution modelling or trait-based ecology. Interest or experience in applying AI or machine learning methods to ecological questions
-
and documented background in machine learning, deep learning, data analysis and programming. Previous experience in research and knowledge in bioinformatics, biophysics, biochemistry, molecular biology
-
include previous research in computational modeling, machine learning applications in genomics, protein structure, participation in bioinformatics projects, or hands-on experience with AI tools applied
-
”, led by Associate Professor Valeria Vitelli. Successful candidates will work on Bayesian models for unsupervised learning when multiple data sources are available, mostly tailored to the case