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Available Positions

3 PhD positions are available at HPC Lab, ISTI-CNR, Pisa (Italy) on the following topics:

  1. Efficiency issues in neural IR (Curiosity-driven research)
  2.  Artificial intelligence at the edge with low-capacity devices (H2020 Teaching)
  3. Smart mobility solutions (H2020 SoBigData++ & EV-CHIP)

Hosting Universities:

ISTI-CNR is involved in several PhD programs run by Pisa universities including

  1. the PhD program in Computer Science (https://dottorato.di.unipi.it/) hosted by the University of Pisa.
  2. the PhD program in Computer Engineering (https://phd.dii.unipi.it/en/) hosted by the University of Pisa.
  3. the PhD program in Data Science (https://datasciencephd.eu/) hosted by the Scuola Normale Superiore (https://www.sns.it/en).

The selected applicants shall also apply to the official call of one of the above PhD schools.

PhD positions

** Position type: doctoral fellowship, 3 years

** Starting date: fall 2020

** Location: HPC Lab @ ISTI-CNR, Pisa, Italy – http://hpc.isti.cnr.it

** Annual scholarship: EUR 15,000 – 17,000 (depending on the program)

Application deadlines:

  • July 10, for PhD programs hosted by the University of Pisa.

  • August 27, for the PhD program hosted by the Scuola Normale Superiore.

Interviews with selected candidates will be organised based on received applications. The positions will be filled as soon as suitable candidates are identified. Interested candidates are thus strongly encouraged to send their application as soon as possible.

For all positions, it will be possible (and advised) to organise one visiting student period abroad (typically, 6 months) during the PhD.

The PhD students will work in strict collaboration with researchers of the High Performance Computing Laboratory (HPC Lab) of ISTI-CNR in Pisa, Italy (http://hpc.isti.cnr.it). HPC Lab conducts research on algorithms and information systems dealing with computational and data-intensive problems in business, social, and knowledge-based applications. HPC Lab research areas include large-scale distributed and cloud systems, efficient information indexing and retrieval, big data analytics, machine learning and artificial intelligence, mobility analysis, information extraction and semantic enrichment. The lab has a strong track record of impactful research and successful activities in European projects which is reflected in the many international collaborations with academia and industrial companies in the EU and US.

Application procedure

Applications should consist of (all documents in English):

*  a complete CV, including exams taken during the University degrees with grades, including, if already completed, the MSc final degree), and a link to the MSc. thesis;

*  a 1-page research statement showing motivation and understanding of the topic of the position;

*  at least one contact person (2 even better) who could act as reference(s).

The applications and any request of information should be sent to: raffaele.perego@isti.cnr.it with subject “PhD application at HPC Lab”

Contact point

For any additional information or clarification, please send a message to raffaele.perego@isti.cnr.it

The open PhD position focuses on novel machine/deep learning techniques for Information Retrieval (IR) and Recommender Systems (RS). As a PhD candidate you will perform cutting edge research in the field of deep learning techniques for next generation context-aware search and recommendation. In the last years, deep pretrained transformer networks have shown to be effective in solving several ranking tasks, such as question answering and ad-hoc document ranking. However, their computational expenses deem them cost-prohibitive in practice. The research will focus on the design and implementation of efficient and scalable neural solutions for IR and RS that can provide effective answers to complex queries by possibly using a limited amount of computational resources. You will develop algorithms and systems that support novel ways for users to search and interact with online platforms for search or recommendation tasks (IR/NLP tasks for conversational systems and personal assistants). The research will be based on a rigorous experimental methodology and the candidate is expected to test newly developed solutions against competitive state-of-the-art baselines on large publicly available benchmarks by using widely accepted evaluation protocols.

Reference HPC Lab researchers for the position are Raffaele Perego, Franco Maria Nardini and Salvatore Trani.

Candidate profile

Candidates should have or about to obtain a MSc degree (at the latest by 31st October 2020) in Computer Science, Computer Engineering, or closely related disciplines, and a proven track record of excellent University grades. Preferably, the topic of the MSc thesis should be in one of the relevant research areas (Information Retrieval, Natural Language Processing, or Machine Learning on Textual Data). Good written and spoken communication skills in English are required.

The open PhD position focuses on the design, implementation and assessment of novel approaches enabling and optimizing the execution of AI algorithms on edge computing environments. The research work investigate novel ways for enabling the tailoring of AI solutions to edge computing environments by taking into account the specific characteristics of the resources available at the edge (e.g., computing capacity, operating systems, network bandwidth, etc.) and their actual interplay with clouds (e.g., orchestration, deployment, offloading, federation). The candidate will have the opportunity to assess the research advancements achieved within the use cases of the TEACHING project (https://www.teaching-h2020.eu), including a challenging use case on autonomous driving. The research will be based on a rigorous experimental methodology and the candidate is expected to test newly developed solutions against competitive state-of-the-art baselines on large publicly available benchmarks by using widely accepted evaluation protocols.

Reference HPC Lab researchers for the position are Patrizio Dazzi, Massimo Coppola and Emanuele Carlini.

Candidate profile

Candidates should have or about to obtain a MSc degree (at the latest by 31st October 2020) in Computer Science, Computer Engineering, or closely related disciplines, and a proven track record of excellent University grades. Preferably, the topic of the MSc/PhD thesis should be in one of the relevant research areas (cloud or edge technologies, distributed, parallel or high-performance computing, efficiency in AI). Good written and spoken communication skills in English are required

The open PhD position focuses on the design and implementation of novel solutions for smart mobility. The focus will be on developing novel algorithms exploiting semantically-enriched  trajectory data, where semantics come from heterogeneous and multi-dimensional information sources, e.g., social media, positioning information, textual data, images, etc.  New methods should take into account the different natures of the data. Examples of these methods might include machine/deep learning techniques for classification, prediction, similarity, and recommendation. Methods to develop smart mobility solutions in a “social distancing” scenario can also be considered. The research will be based on a rigorous experimental methodology and the candidate is expected to test newly developed solutions against competitive state-of-the-art baselines on large publicly available benchmarks by using widely accepted evaluation protocols.

Reference HPC Lab researchers for the position are Chiara Renso, Cristina Muntean and Lorenzo Gabrielli.

Candidate profile

Candidates should have or about to obtain a MSc degree (at the latest by 31st October 2020) in Computer Science, Computer Engineering, or closely related disciplines, and a proven track record of excellent University grades. Preferably, the topic of the MSc thesis should be in one of the relevant research areas (big data analytics, human mobility, social network analysis). Good written and spoken communication skills in English are required.