The HPDC research cluster offers excellent research opportunities for PhD students. Students have opportunities to explore fundamental concepts in parallel and distributed computing, as well as future and emerging areas that cut across hardware, system software and applications. We have several PhD opportunities in the following areas:
- Fundamentals of parallel programming (task-parallel models, macro-dataflow, parallel behavioral skeletons, concurrent data structures, and domain-specific languages)
- Emerging high-capacity memory technologies (DRAM, NVRAM, storage-class memories)
- Energy-efficient -parallel programming and systems software (compiler, runtime or OS-level support for power/energy savings in processing, memory and networking components)
- Uncertainty-aware and approximate parallel computation (programming models, application characterisation, and performance/energy optimisation)
- Software for real-time, streaming data analytics (manycore and accelerator-enabled in-memory databases, and OS/runtime support for scalable transactional/analytical execution)
- Heterogeneous micro-servers for data analytics (hardware/software interface and workload characterisation)
- Abstraction-level energy accounting in many-core distributed and embedded systems (fine-grain energy accounting at the level of basic blocks, variables, data structures and concurrent tasks)
- Energy-proportional heterogeneous computing using OpenCL (language and runtime support for energy-proportional execution on heterogeneous, reconfigurable architectures)
- Real-time data analytics on the Cloud (real-time virtual machine scheduling, accelerator virtualisation, QoS-aware memory allocation)
- Topology-aware cloud resource accounting and management (scalable in-situ logging and analysis of performance, reliability and energy data, holistic scheduler optimisation)
- Many-task programming models for the Cloud (resource elasticity, large-scale data handling, synchronization, locality, scheduling fault-tolerance)
For informal enquiries please contact Professor Dimitrios S. Nikolopoulos.
How To Apply
Applicants should apply online. Visit webpage https://dap.qub.ac.uk/portal/ and follow the instructions to register and apply.