seminari corsi meteorologia
Environmental Meteorology Seminar – Claudia Acquistapace

Per la serie di seminari in meteorologia ambientale dell’Università di Trento giovedì 7 marzo alle ore 14:30 Claudia Acquistapace, dell’Institute for Geophysics and Meteorology University of Cologne, terrà un seminario dal titolo: Exploiting machine learning to improve our understanding of severe storms over the Alps: EXPATS project’s story and future.

Il seminario si terrà in presenza nell’aula 1P (primo piano) al DICAM – Università di Trento, in via Masiano 77, e sarà possibile seguirlo in streaming su Zoom al seguente link:

https://unitn.zoom.us/j/88611750340 

(Meeting ID: 8861175 0340, Passcode: 615808)

Abstract
Severe thunderstorms are crucial in the expected damages due
to climate change and floods that the EU will face in the 2050s. These
systems, especially in the Alps, produce large hail, heavy rain and
cause landslides and flash floods. The project “Exploiting
spatiotemporal cloud patterns to advance severe storms process
understanding and detection (EXPATS, https://expats-ideas4s.com)” was
recently funded by DWD within the IDEAS4S German-Italian cooperation to
improve our understanding of extreme precipitating events over the Alps.
EXPATS will exploit Meteosat second and third-generation satellite data
(MSG, MTG) and ML self-supervised approaches to identify spatiotemporal
cloud evolution patterns that can lead to extreme events and to
characterize large hail spatiotemporal evolution across multi-year
records. ML self-supervised embeddings showed exciting spatial cloud
pattern identification developments from satellite images.
In this talk, we will introduce the main research questions tackled by
the EXPATS research group and introduce you to the tools we use in our
research. We will show some of the challenges we face and some
preliminary results and feasibility tests we performed to identify hail
with the self-supervised machine learning approach.

Bio
Claudia’s path starts in Pisa, where she got her bachelor degree
in general Physics, and then went on in Bologna, where she completed her
master in physics focusing on atmosphere with a thesis on satellite
remote sensing of light precipitation in Europe. Then, she moved to
Cologne for completing a PhD within the Marie Curie initial training
network focusing on drizzle detection but this time from the ground,
using cloud radar Doppler spectra, for which she was awarded with the
Reinhard-Süring-Stiftung 2019 Research award.
 She employed her first post-doc to use her observations to evaluate the
performance of the ICON-LEM model. She was then actively taking part in
the EUREC4A measurement campaign as PI on the research vessel Maria S.
Merian, conducting radar ship-based observations of clouds and rain in
the tropics.
Currently she is completing a master in science communication at the
University of Trento and she will soon start her new position as junior
research group leader in Cologne, coming back to satellite remote
sensing, this time for better understanding extreme precipitation events
using machine learning methods.
She is the science communication manager of the PROBE Cost Action and
she also got funding for outreach projects, like the videodocumentary on
women in science “Wetoo: what they don’t tell you [1]” and for the
realization of an outreach video on atmospheric boundary layer [2],
which was awarded by the AISAM association with a communication price.
seminari corsi meteorologia
In partenza il corso “Machine learning in weather and climate” dell’ECMWF

Sono aperte le registrazioni per partecipare al corso online Machine learning on weather and climate promosso da ECMWF (European Centre for Medium-Range Weather Forecasts) in collaborazione con IFAB (International Foundation on Big Data and Artificial Intelligence for Human Development) che partirà il 9 gennaio.

Il corso, completamente gratuito, è rivolto a chiunque sia interessato a conoscere i recenti sviluppi delle tecniche di machine learning e come queste possono essere applicate alle previsioni meteorologiche e climatiche. L’unico requisito richiesto è una conoscenza di base di meteorologia, statistica e programmazione.

Il corso si articola in tre moduli:

  • un’introduzione al machine learning in ambito meteorologico e climatico
  • un approfondimento sui concetti di machine learning
  • applicazioni pratiche

È possibile seguire uno o più moduli liberamente, al completamento di ognuno dei quali verrà rilasciato un certificato di partecipazione.

Le lezioni si svolgono nell’arco di 10 settimane, per un totale di 36 ore di formazione.

Ulteriori informazioni sul corso si trovano alla pagina https://lms.ecmwf.int/pages/index.html, da cui è anche possibile registrarsi.

opportunità di lavoro
1 posizione presso il Centro Euro-Mediterraneo sui Cambiamenti Climatici (CMCC)

1 Posizione presso il CMCC

Segnaliamo una posizione disponibile presso il Centro Euro-Mediterraneo sui Cambiamenti Climatici (CMCC):

Junior Scientist – Machine Learning/AI Expert (scadenza 15 Novembre 2022)

Maggiori dettagli di seguito:

The CMCC Foundation is a scientific research center on climate change and its interactions with the environment, the society, the world of business and policy makers.

Our work aims to stimulate sustainable growth, protect the environment and develop strategies for the adaptation and mitigation of climate change.

Our Division of Advanced Scientific Computing (ASC) is looking to hire one talented, motivated and proactive Machine Learning/AI Expert to work, within several European Projects, in team with scientists and engineers in computational science, data analytics, machine learning, and climate science. Main responsibilities will include:

  • Perform scientific research in the area of machine learning/AI applied to climate change, extreme events and wildfires;
  • Publish research results in peer-reviewed journals and technical reports;
  • Engagement and contribution to open source software communities.

The job location is CMCC Headquarters in Lecce, Italy. Remote working is considered as an option only from Europe.

We are looking for a motivated person with the following requirements:

  • Ph.D. in Computer Science, Mathematics, Software Engineering, Economics, Data Science, Statistics, or a similar field;
  • 3+ years of research on machine learning/AI (preferable applied to climate science);
  • Strong experience in using Python stack for scientific programming (numpy, pandas, scipy, scikit-learn) and machine learning (pytorch, keras, tensorflow, jax);
  • Experience working with large weather and climate datasets; Experience in writing research papers for peer-reviewed journals;
  • Ability to work both independently and in team; Proficient verbal and written communication skills in English.

Belonging to legally protected categories (ex L. 68/99) will constitute a preferential condition.

Some fiscal benefit could be applied for repatriated workers or foreign researchers/professors, having the requirements defined by Dlgs 147/2015 (for repatriates) or Dl 78/2010 (for foreigners).

We offer the opportunity to join our team through a temporary contract of 12 months starting as soon as possible, with the possibility of contract extension based on performance, results achieved and new project opportunities. After 2 years, we will evaluate the possibility to offer a full permanent position within the Advance Scientific Computing Division. Annual salary ranging from 33k to 42k Euros, comprehensive of benefits, depending on qualification and experience.

For more information on how to apply please visit this link.

opportunità di lavoro
Ricercatore alla Fondazione Bruno Kessler

Il gruppo di ricerca Data Science for Industry and Physics (DSIP) della Fondazione Bruno Kessler cerca un candidato per coprire una posizione triennale nel campo delle tecniche di machine-learning applicate alle previsioni meteorologiche e alla simulazione del sistema terra.

La sede di lavoro è Povo (TN) e la deadline per la presentazione delle domande è il 4 di agosto. I dettagli delle attività e i requisiti per i candidati si trovano sul sito a questo link.