Planetek Italia (12 months out of 18 in total), and is related to the
following topic:

Machine Learning for Space Weather

The proposed research project is concerned with the study of “Space
Weather Phenomena” and the development of knowledge about the mechanisms
and effects of solar-derived perturbative phenomena developing in
circumterrestrial space and impacting the ionized atmosphere
(ionosphere). In the project emphasis is given to the study and modeling
of the dynamics of the ionospheric plasma and the electron density
irregularities in it on a global scale, in order to improve the
capability of long-term (24-48 hours in advance) nowcasting and
forecasting of the ionospheric response to Space Weather events over the
Mediterranean area. The modeling approach is developed through
innovative “machine learning” techniques, recently introduced (Cesaroni
et al 2020), the results of which point to this as a strategy to extend
the time horizon of ionospheric forecasting, a fundamental requirement
for increasing knowledge of Space Weather phenomena in near-Earth space.
In addition, the growing demand for semi-empirical approaches for
real-time mitigation of errors introduced by the ionosphere on
positioning and navigation systems makes the proposed topic a
significant contribution in the area of “services and research for
society” in relation to the strategic objective “Development of a
National Space Weather Service” in the context of developing
countermeasures to contain the negative effect that the irregular and
disturbed ionosphere can have on technological systems in use in modern
society such as, for example, navigation and positioning satellite
systems (GNSS, GLobal Navigation Satellite Systems), trans-horizon HF
radio communications, and L-band satellite communication systems. Such
systems are of interest to a variety of end users who can be identified
as users of the service in which the developed products may be embedded.
Examples of users may include: precision agriculture operators,
operators in the field of mapping, aviation, and radio communications
operators for emergency management in civil defense.

Cesaroni, C., Spogli, L., Aragon-Angel, A., Fiocca, M., Dear, V., De
Franceschi, G., & Romano, V. (2020). Neural network based model for
global Total Electron Content forecasting. Journal of Space Weather and
Space Climate, 10, 11.

The second research grant shall be developed in close collaboration with
GE Avio (12 months out of 18 in total), and is related too the following
topic:

Operative Framework For HPC (Off-HPC)

High-performance computing (HPC) clouds are becoming a complement or, in
some cases, an alternative to on-premise clusters for running
scientific-technical, engineering, and business analytics service
applications. Most research efforts in the area of cloud HPC aim to
analyze and understand the cost-benefit of migrating computationally
intensive applications from on-premise environments to public cloud
platforms. Industry trends show that on-premise/cloud hybrid
environments are the natural path to get the best out of on-premise and
cloud resources. Workloads that are stable from the point of view of
required computing resources and sensitive from the point of view of the
need to protect processed information can be performed on on-premise
resources, while peak computational loads can take advantage of remote
computing resources available in the cloud typically under a
“pay-as-you-go” consumption mode. The main difficulties in using cloud
solutions to run HPC applications stem from their characteristics and
properties compared to traditional cloud services to handle, for
example, standard enterprise applications, Web applications, data
storage or backup, or business intelligence. HPC applications tend to
require more computing power than application services typically
delivered in cloud environments. These processing requirements arise not
only from the characteristics of the CPUs (Central Processing Units),
but also from the amount of memory and network speed to support their
proper execution. In addition, such applications may have a particular
and different execution mechanism than dedicated cloud application
services that instead run 24/7. HPC applications tend to run in batch
mode. Users execute a series of computational jobs, consisting of
instances of the application with different inputs, and wait until
results are generated to decide whether new computational tasks need to
be submitted and executed. Therefore, moving HPC applications to cloud
platforms requires not only a focus on resource allocation in the
infrastructure in use and its optimization, but also on how users
interact with this new environment. Research in the area of cloud HPC
can be classified into three broad categories: (i) feasibility studies
on adopting the cloud to replace or complement on-premise computing
clusters to run HPC applications; (ii) performance optimization of cloud
resources for running HPC applications; and (iii) services to simplify
the use of cloud HPC, particularly for users who are not specialized in
data and information processing and processing technologies. This
research project intends to focus on study activities within the first
category, in which, more specifically, there are four main aspects that
should be considered: (i) metrics used to assess how feasible the use of
HPC cloud is; (ii) resources used in computational experiments; (iii)
computational infrastructure; and (iv) software, which includes both
well-known HPC benchmarks and computational tools, algorithms, or
methodologies related to specific business application cases. Currently,
the company uses HPC applications running mostly on on-premise systems
but faces issues related to the need for greater computational resources
that can be met through flexible and scalable architectures provided by
cloud technologies. The need is to build clear technology and governance
references for cloud or hybrid infrastructures. The research project
will therefore aim to carefully analyze the state of the art of hybrid
HPC solutions, define criteria for benchmarking different solutions,
develop an operational framework that includes the operational and
economic management aspects of a hybrid HPC solution, and finally
implement one or more industrial pilots.

DEADLINE: June 24, 2022
ALL INCLUSIVE GROSS AMOUNT (for 18 months): 29050,50 euro (i.e., 19367
euro annual gross amount)

NOTE: Foreign candidates are strongly encouraged to contact me by email
if they need help/support in order to prepare their application: I will
be glad to assist.

Here you can download an unofficial English translation of the call:
RIPARTI-call

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Prof.  Massimo Cafaro, Ph.D.
Associate Professor of Parallel Algorithms and Data Mining/Machine Learning
Head of HPC Lab https://hpc-lab.unisalento.it
Director of Master in Applied Data Science

  Department of Engineering for Innovation
  University of Salento, Lecce, Italy
  Via per Monteroni
  73100 Lecce, Italy

  Voice/Fax  +39 0832 297371

  Web   http://sara.unisalento.it/~cafaro
  Web   https://www.unisalento.it/people/massimo.cafaro

  E-mail massimo.cafaro@unisalento.it
  E-mail cafaro@ieee.org
  E-mail cafaro@acm.org

INGV
National Institute of Geophysics and Volcanology
Via di Vigna Murata 605
Roma

  CMCC Foundation
  Euro-Mediterranean Center on Climate Change
  Via Augusto Imperatore, 16 – 73100 Lecce
  massimo.cafaro@cmcc.it

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