Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Field
-
6. of this Notice. Preferred factors: Knowledge in machine learning and programming (Python), deep learning (e.g., tensorflow, pytorch) and time-series modeling in marine ecology applications
-
informatics. Research strengths include intelligent systems, data analytics, cybersecurity, digital health, sustainability and human–computer interaction. The Faculty of IT at Monash University provides
-
), machine learning, advanced use of LLMs. Experience with Unix-like environments and software development in the context of large (open-source) software projects is highly valuable. The applicant should be
-
applications for a PhD Student or Postdoc Position (f/m/d) for any of the following topics: Combining non-equilibrium alchemistry with machine learning Free energy calculations for enzyme design Permeation and
-
mixed reality (MR) strategies to blend information derived from machine learning and computer vision processes to the workers' expertise. The objective of this project is to develop and evaluate MR
-
of imaging data such as structural MRI and functional MRI, preferably ultra-high field imaging is required Experience in machine learning methods and analyzing big datasets is desirable Experience in
-
that combine principled reasoning with the efficiency of modern machine learning to enable intelligent, real-time decision-making in large-scale interconnected systems. This position offers the opportunity
-
selection criteria Peer-reviewed publications in relevant fields. Experience with modelling and simulation, e.g. machine learning, parametric design, or finite element tools (Abaqus, Ansys, etc.). Relevant
-
-based control and decision-making for complex multi-agent systems. The project explores new computational frameworks that combine principled reasoning with the efficiency of modern machine learning
-
learning tools to recommend reaction conditions for the synthesis of novel TRPA1 inhibitors. The project “A machine learning approach to computer assisted drug design” is led by Docent Juri Timonen