PhD Position F/M Vehicle-and-mobile phone computing sharing as part of the edge-to-cloud continuum
il y a 4 semaines
Contexte et atouts du poste
The ANR FITNESS project, part of the PEPR Network of the Future, funds this Ph.D. program.
Mission confiée
ContextIntelligent Transport System is open to new applications and services (e.g., infotainment, video streaming, etc.) leveraging vehicle and consumer interaction opportunities. Among such opportunities is the possibility of using computation and/or connectivity resources offered by nearby intelligent vehicles to execute tasks from third-party devices (mobile phones), thus extending the existing Edge-Cloud ecosystem.
Unfortunately, one of the issues making such resource sharing a challenging task is the vehicles and devices heterogeneity in behaviors and resources (e.g., diverse mobility routines, urban traffic, heterogeneous space-time interaction between users and nearby vehicles, etc.). In particular, a direct consequence of hosting or using resources in cars in a distributed way is their exposure and sensitivity to uncertainties of behaviors in users' mobility and vehicle connectivity brought by traffic conditions. It is, therefore, essential to integrate mobility into the provided solutions besides dealing with resources, capabilities, and sharing.
Objective
The objective of this Ph.D. is first to learn and understand (i) the needs of devices around vehicles, (ii) the resources that vehicles around devices offer, and (iii) how the crowd of devices (or a crowd of vehicles) on the resource-sharing zone evolves in space and time? Second, we are assessing the feasibility of deactivating some of the resources at the edge, including base station antennas and some of the edge node computing servers, to optimize resource utilization, reduce energy consumption, and enhance overall operational efficiency. This strategic adjustment aligns with our goal of achieving a more sustainable and cost-effective infrastructure while maintaining the desired level of service and performance.
Principales activités
Read and synthesize literature work, Propose novel approaches, technical solutions Design, test, and simulate proposed solutions using simulation tools and, if possible, experimental platforms. Write research papers and reports Present the research works
Compétences
Technical skills and level required:
Candidates must have a Master of Science or equivalent degree in Computer Science or Electrical Engineering. Strong background in statistical analysis and tools, mobile networking, and communication networks. Strong knowledge of machine learning algorithms and AI; Past experience in implementing and applying ML/AI algorithms is very welcome. Candidates should be able to perform good critical analyses of obtained results and be creative in proposing solutions. Excellent programming skills (e.g., Python, C, C++) and Data Visualisation Tools (Pandas, etc) Good English skills, both in written and oral form
Avantages
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 (sick children, moving home, etc.) Possibility of teleworking and flexible organization of working hours Professional equipment available (videoconferencing, loan of computer equipment, etc.) Social, cultural and sports events and activities Access to vocational training Social security coverage
Rémunération
1st et 2nd year : 2100€ gross/month
3rd année : 2190€ gross/month
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