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ICCAC DS 2017 : International Conference on Cloud and Autonomic Computing (ICCAC) Doctoral Symposium

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Link: http://autonomic-conference.org/iccac-2017/calls.html
 
When Aug 18, 2017 - Aug 22, 2017
Where Tucson, AZ
Submission Deadline Jun 2, 2017
Notification Due Jun 30, 2017
Final Version Due Jul 21, 2017
Categories    autonomic computing   cyber-physical systems   machine learning   self-adaptive systems
 

Call For Papers

Foundations and Applications of Self* Systems (FAS*) is the umbrella for two closely related but independent conferences, the International Conference on Self-Adaptive and Self-Organizing Systems (SASO) and the International Conference on Cloud and Autonomic Computing (ICCAC). The FAS* Doctoral Symposium provides an international forum for PhD Students working in research areas addressed by FAS*. In this forum, PhD students will get unique opportunities to subject their research to the scrutiny of external experts, gain experience in the presentation of research, connect to peers and experts addressing similar problems, and get advice from a panel of internationally leading researchers. Different from the technical tracks of the conference, the FAS* Doctoral Symposium focuses on the specific needs of young researchers at the beginning of their career. As such, emphasis will be placed on a critical and constructive feedback that shall help participants to successfully conclude their PhD studies.

PhD students working in any area addressed by cloud and autonomic computing are invited to submit a Doctoral Symposium paper in which they describe the key motivation and objectives of their research project, and reflect on the methodology as well as the status of their PhD studies. Complementing the thematic focus of the conference, we particularly solicit contributions in the following areas:

* Autonomic and cloud computing
* Self-* approaches in Massive-Scale Decentralized Systems
* Complex Cyber-physical and socio-technical systems
* Application of Self-* in Robotics and Spatial Computing
* Engineering of self-organizing and self-adaptive systems
* Scalable and applied machine learning / data mining

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