Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Program
-
Employer
- Minnesota State Colleges and Universities, System Office
- University of Toronto
- Fraunhofer-Gesellschaft
- NIST
- Nature Careers
- Northeastern University
- Stanford University
- Technical University of Denmark
- University of Groningen
- ;
- Aalborg University
- Bilkent University
- ETH Zurich
- Ghent University
- Linköping University
- Medical College of Wisconsin
- Minnesota State University
- UNIVERSITY OF HELSINKI
- University of British Columbia
- University of Idaho
- University of Liverpool
- University of Luxembourg
- University of Michigan
- University of Southern Denmark
- 14 more »
- « less
-
Field
-
with chemists and materials scientists is essential to ensure that the developed methods make optimal use of domain expertise and integrate fully into “human in the loop” workflows. This post is
-
are exploring which methods are suitable under which conditions to solve combinatorial optimization problems better than purely classical methods. Possible topics for a master's thesis include, for example
-
essential to ensure that the developed methods make optimal use of domain expertise and integrate fully into ¿human in the loop¿ workflows. This post is available for two years. Keywords: Geometric Deep
-
essential to ensure that the developed methods make optimal use of domain expertise and integrate fully into “human in the loop” workflows. This post is available for two years. Keywords: Geometric Deep
-
large-scale optimization and theoretical combinatorial optimization, algorithmic reasoning remains a significant challenge for artificial intelligence. Our lab’s research is driven by the observation
-
or according to agreement. Short Introduction We are recruiting a Doctoral Researcher to the project “Systems Pharmacology for Rational Combinatorial Cancer Therapy” in the Systems Pharmacology Jafari Lab
-
], which states that random neural networks can be pruned to approximate a large class of functions without changing the initial weights. We are also interested in Neural Combinatorial Optimization, where we
-
hits using in vitro (cell lines, organoids) and in vivo (mouse xenograft/PDX models) systems. They will optimize protocols for NGS library preparation, CRISPR-Cas9 editing, and high-throughput molecular
-
combinatorial panning methods, including phage and mRNA display, to identify de novo peptides for promising biomarkers lacking a natural ligand or lead structure. We then optimize peptide ligands for affinity and
-
University Seattle campus. Course topics include High-performance Computing, VLSI Design, Advanced Machine, Learning Combinatorial Optimization and Statistical Inference. Responsibilities include preparation