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FAPESP Post-doctoral Fellowship #1 - Call for applications
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The FAPESP project "SMART: Sustainable Management of Agriculture with the Intelligent Computing Continuum" (bv.fapesp.br/en/auxilios/116446http://bv.fapesp.br/en/auxilios/116446) has an open Post-Doctoral position to work at the Federal University of ABC (UFABC).
The research theme is "Smart Application Deployment for the IoT Computing Continuum", dealing with the challenges of deploying smart agriculture applications over the IoT Computing Continuum (IoTinuum), considering the inherent distribution of the end-to-end path between devices and the cloud and the need for individual services to run in multiple stages in-between. The SMART project aims to develop and deploy a trustworthy platform for smart agriculture across the IoTinuum, from field sensors to the cloud. It will utilize various sensors, data sources, networking technologies, data management, and machine learning to ensure reliable dataflow and trustworthiness. The deployment of smart applications for the IoTinuum requires the efficient orchestration of resources allocated to the services to provide the intended quality levels. Orchestration involves fulfilling the application needs by carefully matching services, resources, and workloads, including microservice placement, deployment, and migration. In the IoTinuum context, it means being aware of and monitoring the resources available in each stage, understanding the application QoS requirements (such as maximum delay), and deploying the services in the most appropriate stage, i.e., in Mist, Fog, or Cloud. An IoT smart application comprises various services (i.e., microservices), which may be deployed in different stages for distinct installations. Using a certain node within the continuum stage involves deploying services. If this stage is not used in a given installation, this service must be deployed elsewhere in another stage. Current deployment strategies cannot handle varying configurations for IoT efficient and scalable application deployment within the continuum.
The general objective of this postdoc fellowship is to design and implement an application deployment methodology for the IoTinuum that takes as input a model (such as a directed acyclic graph) representing the application and generates a deployment strategy as output. The main challenges in this proposal involve:
1. A Deployment Triggering strategy, which identifies the need for a new deployment to be executed over the IoTinuum
2. A Deployment Biding strategy, which means that that abstract graph representing the application will be mapped to specific nodes (with IP addresses and ports) located in different stages of the continuum, such as Thing, Mist, Fog, or Cloud
3. A Deployment Distribution and installation strategy for transferring services to where they will be executed (e.g., cloud or fog)
4. A preliminary version of the application deployment methodology
A highly motivated candidate is expected to show background in the following concepts:
- IoT, cloud, fog, edge, and the computing continuum
- Application deployment and DevOps strategies
- Performance analysis with experimentation techniques
- Advanced programming language skills
- Advanced English language skills
- Strong publication record
The candidate must hold a Ph.D. in Computer Science, Electrical Engineering, or related areas (less than 7 years since graduation). The fellowship's duration is 30 months, and the monthly stipend is R$12.000,00 plus 10% for research expenses.
The term for this scholarship is expected to start as soon as possible, ideally in April 2025.
Candidates should fill out the online form at:
https://forms.gle/AxC47yMXqFi9fP9K8https://cas5-0-urlprotect.trendmicro.com/wis/clicktime/v1/query?url=https%3a%2f%2fforms.gle%2fAxC47yMXqFi9fP9K8&umid=7eb97f8b-9fa2-4b15-b83a-bc34b657fb9c&auth=970bcda4d89f6a02a247db638cee976611706e18-aa4c4c83790c3ad8779ef397151601532f303b7b
Deadline: March, 14, 2025
Inquiries can be sent to c**************i@ufabc.edu.brmailto:c**************i@ufabc.edu.br
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FAPESP Post-doctoral Fellowship #2 - Call for applications
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The FAPESP project "SMART: Sustainable Management of Agriculture with the Intelligent Computing Continuum" (bv.fapesp.br/en/auxilios/116446http://bv.fapesp.br/en/auxilios/116446) has an open Post-Doctoral position to work at the FEI Univerity Center (FEI).
The research theme is "Developing the Intelligent IoT Continuum for Precision Agriculture: Task Allocation, Forecasting, Resource Management, and Data Analytics", dealing with the challenges of deploying smart agriculture applications over the IoT Computing Continuum (IoTinuum).
This project aims to develop an intelligent Internet of Things (IoT) system focused on precision agriculture. The aim is to create an interconnected network of devices capable of collecting and analyzing data on crop conditions, such as soil moisture, temperature, and weather conditions. Using artificial intelligence techniques, the system should be able to optimize the allocation of tasks between the different devices in the network, manage resources efficiently, and carry out predictive analyses of agricultural production. The system will train machine learning and deep learning models collaboratively through federated learning, using data from different devices without centralizing the information. Based on the data collected and analyzed, the system should predict the production of a given crop, optimize irrigation and fertilizer application, and identify possible crop problems in advance. In short, this project aims to create an innovative solution for agriculture, combining the Internet of Things with artificial intelligence to improve productivity, reduce costs, and promote sustainability.
The general objective of this postdoc fellowship is to develop an intelligent Internet of Things system for precision agriculture that can collect, analyze, and utilize data on crop conditions. The main challenges in this proposal involve:
1. To design an intelligent IoT system for precision agriculture.
2. To optimize the allocation of tasks and resources within the IoT network using artificial intelligence techniques.
3. To perform predictive analyses of agricultural production through machine learning and deep learning models trained collaboratively using federated learning.
4. To predict crop production, optimize irrigation and fertilizer application, and identify potential crop problems.
A highly motivated candidate is expected to show background in the following concepts:
- Machine Learning, Deep Learning, Neural Network Architectures, time series and their application to agricultural problems. Federated Learning is a plus
- Data Analytics, Model Evaluation and Deployment
- IoT, cloud, fog, edge, and the computing continuum
- Advanced programming language skills
- Advanced English language skills
- Strong publication record
The candidate must hold a Ph.D. in Computer Science, Electrical Engineering or related areas (less than 7 years since graduation). The fellowship's duration is 30 months, and the monthly stipend is R$12.000,00 plus 10% for research expenses.
The term for this scholarship is expected to start as soon as possible, ideally in April 2025.
Candidates should fill out the online form at:
https://forms.gle/oftX3orZz478Gne17https://cas5-0-urlprotect.trendmicro.com/wis/clicktime/v1/query?url=https%3a%2f%2fforms.gle%2foftX3orZz478Gne17&umid=7eb97f8b-9fa2-4b15-b83a-bc34b657fb9c&auth=970bcda4d89f6a02a247db638cee976611706e18-deb6d21e2ebba999a08d4c6845cfc53ae8908df0
Deadline: March, 14, 2025
Inquiries can be sent to r******i@fei.edu.brmailto:r******i@fei.edu.br Esta mensagem, juntamente com qualquer outra informa??o anexada, ? confidencial e protegida por lei. Somente os seus destinat?rios est?o autorizados a us?-la. Se voc? n?o for o destinat?rio, por favor, informe o remetente e, em seguida, apague a mensagem, observando que n?o h? autoriza??o para usar, copiar, armazenar, encaminhar, imprimir ou tomar qualquer a??o baseada no seu conte?do.