Ofertes de projectes

In many state-of-the-art simulation codes the discretization is so closely tied to the data layout and solver that switching discretizations in the same code is not possible. Not only does this preclude the kind of comparison that is necessary for scientific investigation, but it makes library development impossible. This project consists of implementing and verifying different topology strategies to treats all the different pieces of a 3D mesh (e.g. cells, faces, edges, and vertices) in exactly the same way. This allows the mesh interface to be very small and simple while remaining flexible and general. This also allows "dimension independent programming", which means that the same algorithm can be used unchanged for meshes of different shapes and dimensions. The project will use an existing parallel python prototype and explore alternatives to improve its robustness and extend it without sacrificing flexibility. This project will investigate various ways to optimize and parallelize Python programs for large-scale simulations on real-life production clusters. This project will be developed in the context of the PIXIL project (Interreg POCTEFA), which is coordinated by the geosciences applications group of the Barcelona Supercomputing Center.

Robotic Process Automation is receiving significant attention, due to the promise of improving the performance of the main processes of an organization by incorporating robots that partially perform repetitive tasks. In this project, we will consider how Process Mining can help into finding opportunities to apply Robotic Process Automation for a real case study.

From a snow avalanche model developed at the UPC, which simulates the dynamics of this phenomenon, we want to do a full validation of the model to allow specialists in avalanches of the ICGC to use it as a tool in the decision-making process.

Languages follow many statistical regularities called laws. Perhaps the most popular example is Zipf's law for word frequencies, that relates the frequency of a word with its rank, but other laws have been formulated, such as the law of abbreviation, the law of meaning-distribution, the meaning-frequency law,...and so on (Zipf 1949). About 15 years ago, a family of optimization models was introduced to shed light on the origins of Zipf's law for word frequencies (Ferrer-i-Cancho & Solé 2003, Ferrer-i-Cancho 2005). In that family, language is modelled as a bipartite graph where words connect to meanings and a cost function is defined based on the structure of that graph. A simple Monte Carlo algorithm was used to minimize the cost function while the structure of the graph was allowed to vary. Recently, it has been shown how these models shed light on how children learn words (Ferrer-i-Cancho 2017). The aim of this project is to investigate new versions of these models (e.g., Ferrer-i-Cancho & Vitevitch 2018) in two directions: (1) Providing an efficient implementation of the optimization algorithm. (2) Comparing the statistical properties of the model against the statistical properties of natural communication systems.

FHIR (Fast Healthcare Interoperability Resources) is a set of standards developed by HL7 International to facilitate eHealth information interoperability and use. On the other hand, different efforts are in place to improve the representation (more compression and security) of Genomic information, such as those from the GA4GH (Global Alliance for Genomics and Health) and the MPEG standardization committee. The DMAG (Distributed Multimedia Applications Group) of the Computer Architecture Department of the UPC is involved in the specification of some of these new standards. The objective of this project is to integrate genomic information into EHRs (Electronic Health Records). For this purpose, the different standards for the representation of medical and genomic information will be analysed, and FHIR will be used to faclitate that integration. Finally, a small prototype will be developed, probably making use of existing open source software. The results of this work could be contributed to one of the different standardization organizations for its consideration.

The weather - dependent routing algorithm will be integrated into a service for the logistic platforms of food distribution companies enabling self-response capabilities during severe weather events. Using a Multi-Hazard Early Warning System (MH-EWS) as an input, effects of weather will be crossed with a representation model of the road network. The model will be used to provide alternative routes with anticipation to the logistic demand (total freight to be moved between warehouses). Resulting routes should be shown over a map.

The goal of this project is to create a security module for the Open Daylight platform [1] using a tool similar to Bitcoin called Hyperledger [2]; both of them are open-source projects. Taking advantage of the security properties of Bitcoin (distributed, trustless, auditable), it is possible to securely assign IP addresses among its owners, allowing the detection of parties that are not using their legitimate IP addresses. In addition, with the extended capabilities of Hyperledger, we can distribute more information rather than just IP prefixes, such as public keys, configuration parameters, etc. The student will get involved in the joint development team between Cisco Systems (HQ, San Jose CA) and UPC.

In this project the aim is to implement and evaluate some agile optimization methods for city logistics that meet real time and large scale requirements.

Web tracking technologies are extensively used to collect large amounts of personal information (PI), including the things we search, the sites we visit, the people we contact, or the products we buy. Although it is commonly believed that this data is mainly used for targeted advertising, some recent works revealed that it is exploited for many other purposes, such price discrimination, financial credibility, insurance coverage, government surveillance, background scanning or identity theft. The main objective of this project is to apply network traffic monitoring and analysis technologies to uncover the particular methods used to track Internet users and collect PI. This project will be useful for both Internet users and the research community, and will produce open source tools, real data sets, and publications revealing most privacy attempting practices. Some preliminary results of our work in this area were recently published in Proceedings of the IEEE (IF: 9.237) and featured in a Wall Street Journal article.

Gran parte del código abierto para big-data está escrito para la JVM y actualmente gran parte de este código lo forman algoritmos de minería de datos, y otras técnicas que pueden incluirse en la especialidad de Inteligencia Artificial. Además de Java, Python, otro lenguaje interpretado, es cada vez más utilizado en cualquier entorno de programación, en concreto también en inteligencia artificial y para algoritmos de machine learning. Hay adeptos da cada uno de los dos lenguajes, basándose en su curva de aprendizaje, su portabilidad, ... Es open source, portable y soporta tareas estándar de la minería de datos, como es el pre-procesado de datos, la clasificación, agrupación en clústeres, visualización, regresión y selección de características. El objetivo principal de este proyecto es: A partir de un conjunto de algoritmos que caracterizan el trabajo a realizar en minería de datos, tales como pre-procesado de datos, la clasificación, agrupación en clústeres, visualización, regresión y selección de características, compara el rendimiento de estos dos lenguajes y ver las ventajas o inconvenientes de utilizar uno u otro, según la plataforma hardware subyacente. En concreto, x86 y ARM.

The Barcelona Neural Networking Center (BNN-UPC) is offering two positions to develop the Master Thesis in the field of Graph Neural Networks (GNN) applied to computer networking. This TFM will be fully funded and will be carried in the context of a large industrial project with a major multinational technology company.

The main goal of this project is to develop a network monitoring system that can be used by network operators to detect bitcoin miners (or miners from other blockchain technologies) in their network. The system will rely only on network measurements obtained by standard network measurement tools and estimate interesting characteristics of detected miners, such as power consumption. How to apply: Please send an email to with your CV and academic file (pdf can be generated from the Raco).

PETGEM is an open-source 3D electromagnetic modeler with support for HPC architectures. It solves Maxwell's equations using a high-order vector finite element method. PETGEM is mostly written in Python. On the other hand, OpenGeode is an open-source platform for representing and manipulating geometric models. This project consists of the integration of OpenGeode within PETGEM in order to improve the main work-flow for electromagnetic modeling in geophysics. The project will use real-life data sets and explore alternatives to extend it without sacrificing flexibility. Among the topics are refactoring in software engineering, Python programming (efficient use of Numpy arrays), and parallel computing with message passing approach (mpi4py). This project will be developed in the context of the PIXIL project (Interreg POCTEFA), which is coordinated by the geosciences applications group of the Barcelona Supercomputing Center.

The identification of the applications behind the network traffic (i.e. traffic classification) is crucial for ISPs and network operators to better manage and control their networks. However, the increasing use of encryption and web-based applications makes this identification very challenging. This problem is exacerbated with the widespread deployment of content distribution networks (e.g. Akamai) and cloud-based services (e.g. Amazon AWS). The goal of this project is to develop a traffic monitoring tool to accurately identify web services from HTTPS traffic, including Google, YouTube, Facebook, Twitter among others. The tool will combine the information from IP addresses and DNS, with novel classification methods inspired on the Google PageRank algorithm to identify encrypted traffic, even if served from Akamai, AWS or Google infrastructures. This project will be carried out in collaboration with the tech-based company Talaia Networks (https://www.talaia.io), which develops cloud-based network monitoring solutions.