PhD Position F/M in Probabilistic Embedded Systems for Efficient AI Execution
il y a 4 semaines
Context and Benefits of the Position
The PhD thesis is funded by the Paris region program and hosted by the Kopernic team in Paris. The successful candidate will have the opportunity to work on a cutting-edge project that combines probabilistic and non-probabilistic models to deal with the time validation of real-time critical systems.
Research Objectives
The main objectives of the thesis are to propose efficient scheduling algorithms for probabilistic embedded systems on hybrid architectures, to compare their energy performances with existing non-probabilistic algorithms, and to respect time constraints. The results will be illustrated on the Kopernic benchmarks - KDBench.
Main Activities
- State of the art on probabilistic and statistical approaches as well as non-probabilistic architectures for hybrid architectures.
- Proposal of multicore scheduling algorithms for Directed Acyclic Graph tasks on hybrid architectures where some tasks implement learning methods.
- Proposal of energy-relevant versions of proposed algorithms.
- Validation of the results on a case study proposed by StatInf, as well as on an open-source benchmark.
Requirements
The successful candidate should have a background in real-time systems, embedded systems, and programming languages such as Python and C/C++. Knowledge of English and French is required.
Benefits
- Subsidized meals
- Partial reimbursement of public transport costs
- Leave: 7 weeks of annual leave + 10 extra days off due to RTT (statutory reduction in working hours) + possibility of exceptional leave
- Possibility of teleworking and flexible organization of working hours (after 12 months of employment)
- Professional equipment available (videoconferencing, loan of computer equipment, etc.)
- Social, cultural, and sports events and activities
- Access to vocational training
- Social security coverage
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