Sort by
Refine Your Search
-
Listed
-
Category
-
Employer
-
Field
-
work. A model is to be developed to estimate the material mass breakdown for various cell designs and cell formats. The model will be validated from teardown analysis of commercial lithium-ion battery
-
) workflows for learning from large-scale imaging and molecular data Develop ML models to investigate cellular responses, particularly in cancer cell lines Develop DL models for molecular design based on time
-
research in Europe. Research at UPSC covers a wide range of disciplines in plant biology including ecology, computational biology, genetics, physiology, biochemistry, cell biology and molecular biology (see
-
totaling 60 ECTS credits) and join an international research team with backgrounds in sociology, political science, network science, statistics, and machine learning. More information on the PhD program can
-
environments.DutiesThe PhD student will carry out research in the area of cooperative autonomous systems. The successful candidate will explore topics such as: Multi-agent reinforcement learning Distributed control
-
position for candidates interested in interpretable AI, stochastic optimal control, deep learning and high-impact research in sustainable mobility. About us The position is located at the Systems and Control
-
involves collecting clinical data on the effects of childhood cancer treatment, bioinformatically handling sequence data and developing prediction models, as well as conducting Single Cell RNASeq studies and
-
application! Your work assignments Our research projects focus on distributed sensing, hardware-efficient signal processing, robustness and resilience, and communication-efficient decentralized machine learning
-
We are offering a PhD student position in machine learning (ML) theory, focusing on new methods for training models with a limited amount of data. The student will be a part of a new NEST initiative
-
and machine learning to tackle the complexity of force allocation and motion planning under uncertainty and actuator failures. The project combines theoretical research in stochastic optimal control