The candidate is expected to design, implement, and evaluate a tool to provide Transactional Support for Microservices and FaaS architectures. The main focus will be on the support of Transaction Causal Consistency, but it will also be considered the possibility of providing stronger consistency guarantees, in particular Snapshot Isolation.

Candidates should have a Ph.D. in Applied Mathematics, Physics, Computer Science or a related field. They should possess a solid knowledge in applied mathematics, numerical methods, good programming skills, and a proven publication record (minimum 5 papers published in well reputed journals in the field of scientific computing). Successful candidates will be able to work well within small cross-disciplinary teams, including working with researchers using both theoretical and computational mathematics. Experience with common scientific computing methods, computational tools, mathematical software, and parallel computing is highly desired. The successful Post Doctoral Fellow will interact closely with national and international collaborators and will have the opportunity to publish in high impact journals, present their work at international conferences and gain access to major supercomputing facilities through existing networks of collaborative partners. This is a one-year appointment, renewable up to three years. There are no teaching duties associated with this position

The project has the following objectives: Improving and Adapting VR/AR applications for Medical Imaging and Health Applications Perform user tests with students, clinicians, and radiologists in the context of the XAVIER project Help with Data analysis and validation of the applications to support the publication of results

The main goal of this work will consist in the design and development of the solutions to address the project’s challenges of text summarization, entity extraction, text indexing and retrieval.

The objective and workplan consist of a preliminary study of the energy efficiency of the serverless computing model. In particular, the grant holder should study the energy scalability of serverless platforms as the load that those platforms support increases, and, based on the observations from that study, propose a new design for energy-efficient FaaS, and start working on a preliminary implementation.

In project VIRHOSTAI, we will take leverage of state-of-the-art AI advances, specifically designed Natural Language Processing (NLP) models and Deep-Learning (DL) algorithms to link in an interdisciplinary approach omics and biophysical traits to predict virus and human Drug-Target Interactions (vhDTIs) and their networks. We will draw from genomic, proteomic, metabolic, and transcriptomic profiles of virus and humans’ drug-target molecules largely available in open sources as well as other broader but also of interest for the case. The models will then be used to predict targets for antiviral drugs. The main objective of this scholarship is then to select and apply suitable DL models to the data already collected, and to evaluate them on the mentioned prediction task.

The goal of this thesis is to develop a sequence alignment algorithm for circular sequences based on the FM-Index data structure. This work will involve working with algorithms and data structures for indexing and string matching and consists on the implementation and testing of a tool to perform string alignment between large numbers of circular sequences; The working alphabet is the regular DNA letters (A, C, G, T, and N) and implementation should be done in a fast low-level programming language such as C or C++. Additional multi-threading and parallelization is optional but welcome. A visualization component for the final alignment is not required, but also welcome.

The goal of this project is to contribute to the development of TorCloak, a new pluggable transport (PT) for the Tor anonymity network. TorCloak will enable censorship-resistant tunneling of Tor traffic through WebRTC-based covert channels. It will provide strong resistance against traffic analysis attacks based on state-of-the-art machine learning techniques. Specifically, this project will be focused on building a web and mobile frontend for the current TorCloak prototype. Concretely, this involves a) designing a web and mobile US, b) implementing a web application and equivalent mobile, and c) producing documentation and tutorials of the developed applications.

For more information go to: https://euraxess.ec.europa.eu/jobs/81257