Machine Learning in Finance via Randomization

Friday, June 10th 2022 | 10:00 am (CET) | Room: N.2.35 |

Josef Teichmann | Prof. at ETH Zürich


Randomized Signature or random feature selection are two instances of machine learning, where randomly chosen structures appear to be highly expressive. We analyze several aspects of the theory behind it, show that these structures have several theoretically attractive properties and introduce two classes of examples from finance (joint works with Christa Cuchiero, Lukas Gonon, Lyudmila Grigoryeva, Martin Larsson, and Juan-Pablo Ortega).


Professor at ETH Zurich since 2009, Research Interests include Mathematical Finance, Machine Learning in Finance and Stochastic Analysis, Executive Secretary of the Bachelier Finance Society.

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Studienpraktika E-Business

Praxiserfahrung mit Energie!

Praktikumsdauer 08/2022 bis 02/2023


  • Inhaltliche Wartung von Internetseiten (keine Programmierung!)
  • Umsetzung und Auswertung von Online-Kampagnen
  • Mitarbeit bei Newsletter-/Social-Media-Aktivitäten
  • Eigenständige Konzeption von kleinen bis mittleren Digitalprojekten
  • Zusammenarbeit/Koordination Webagenturen
  • Auswertung von Kennzahlen und Berichterstellung
  • Qualitätssicherungsmaßnahmen
Dieses Bild hat ein leeres Alt-Attribut. Der Dateiname ist grafik-4.png

Interessiert? Weitere Informationen und zur Bewerbung gehts hier!

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Advances in Visual Quality Restoration with Generative Adversarial Networks

Thursday, May 12th 2022 | 2 pm (CET) | Room: HS 6 |

Leonardo Galteri, PhD | University of Florence

Abstract: In the latest years, we have witnessed a growing number of media transmitted and stored on computers and mobile devices. For this reason, there is an actual need to employ smart compression algorithms to reduce the size of our media files. However, such techniques are often responsible for severe reduction of user perceived quality. In this talk we present several approaches we have developed to restore degraded images and videos to match their original quality, making use of Generative Adversarial Networks. The aim of the talk is to highlight the main features of our research work, including the advantages of our solution, the current challenges and the possible directions for future improvements.

Bio: Leonardo Galteri is a Postdoctoral Researcher and Adjunct Professor at the University of Florence.

His research activity is focused on computer vision and pattern recognition techniques. Most of his work involves image and video reconstruction, compression artifact removal and noise removal.

In 2018 he obtained the title of PhD, presenting a thesis on the detection of objects in compressed images and videos using techniques based on deep learning. Throughout his research activity, he has participated in various European, national and technology transfer projects with different responsibilities. He is co-founder and Head of Engineering at Small Pixels s.r.l., a startup company that offers technological solutions for real-time video restoration and enhancement.

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Unterstützung bei Projekten in der Automatisierung

Infineon Technologies - Semiconductor and System Solutions

Wir sind auf der Suche nach motivierten und engagierten Studierenden (w/m/div)* zur Unterstützung unseres internationalen Teams in Villach. Ein spannendes Arbeitsumfeld zeichnet dieses Praktikum ebenso aus wie eine attraktive Entlohnung. Verstärken Sie unser Team!

Zu ihren neuen Aufgaben behören u. a.:

  • First point of contact als Schnittstelle zwischen Testwafer Bereitstellung und Produktion
  • Identifikation und Analyse des Verbesserungspotentials bei Testscheiben in der Produktion
  • Aufbau und Durchführung begleitender Tests in der Produktion
  • Support bei Reports in Tableau (SQL)
  • Erstellen von Online-Trainingsunterlagen in englischer Sprache
  • Aktive Mitarbeit bei der Wiki Wartung

Beschäftigungsart: Befristet / Teilzeit (Flexible Arbeitszeit von Montag bis Freitag zwischen 06:00 und 19:00 Uhr)

Dauer: mind. 6 Monate

Weitere Informationen finden Sie hier!

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ROBOTICS Safety Day – Workshop rund um Robotersicherheit

28.04.2022 | 10:30-20:00 Uhr |

JOANNEUM RESEARCH Institut ROBOTICS, Lakeside B13b | Klagenfurt am Wörthersee

Teil 1 – ROBOTICS Safety Day am Vormittag

10:30 – 11:00 Impulsvortag über Kraftmessungen

11:00 – 12:30 Hands-on Kraftmessungen (zusätzliche Voranmeldung zur Durchführungsplanung erwünscht)

Teil 2 – ROBOTICS Safety Day am Nachmittag

12:30 – 13:30 Round Table „Maschinensicherheit“

13:30 – 14:15 Workshop Robotersicherheit & Ausblick Forschungsthemen

14:15 – 14:30 Pause

14:30 – 15:00 Update in der Normung

15:10 – 15:45 Zukunftstechnologien für Speed & Separation Monitoring

Teil 3 –  ab 16 Uhr Workshop „Produktive Mensch-Roboter-Interaktion mit Hilfe von Sensoren“

in Kooperation mit der Alpen-Adria-Universität im Zuge des CapSize Projekts (gefördert von EFRE und KWF)

16:15 – 17:15 Impulsvorträge aus Wissenschaft/Industrie und Forschungsvision CapSize

17:15 – 17:30 Pause

17:30 – 18:15 Sensorik und Robotik Demosession im Labor

18:15 – 18:45 Round Table – Anwendungsperspektiven Umfeldwahrnehmung

ab 18:45 Get together im ROBOTICS Solutions Center

Nähere Informationen zur Veranstaltung / zur Anmeldung finden Sie hier!

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Trends in Recommendations Systems – A Netflix Perspective

Thursday April 7th 2022 | 05.30 pm (CET) | via Zoom

Anuj Shah, Ph. D. | Senior Machine Learning Research Practitioner at Netflix


Recommendation systems today are widely used across many applications such as in multimedia content platforms, social networks, and ecommerce, to provide suggestions to users that are most likely to fulfill their needs, thereby improving the user experience. Academic research, to date, largely focuses on the performance of recommendation models in terms of ranking quality or accuracy measures, which often don’t directly translate into improvements in the real-world. In this talk, we present some of the most interesting challenges that we face in the personalization efforts at Netflix. The goal of this talk is to sunshine challenging research problems in industrial recommendation systems and start a conversation about exciting areas of future research.


Anuj Shah is a Senior Machine Learning Research Practitioner at Netflix. For the past 10+ years, he’s been working on an applied research team focused on developing the next generation of algorithms used to generate the Netflix homepage through machine learning, ranking, recommendation, and large-scale software engineering. He is extremely passionate about algorithms and technologies that help improve the Netflix customer experience with highly personalized consumer-facing products like the Continue Watching row, the Top 10 rows amongst many others. Prior to Netflix, he worked on machine learning in the Computational Sciences Division at the Pacific Northwest National Laboratory focusing on technologies at the intersection of proteomics, bioinformatics and Computer Science for 8 years. He has a Ph.D. from the Computer Science department at Washington State University and a Masters in C.S. from Virginia Tech.

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Introduction to 5G from radio perspective

Ms. Thura Hatim Al-Juboori | Ericsson Poland | Friday December 3, 2021 | 10:00 (CET, 09:00 UTC) |

Click here to join the meeting


5G is the fifth generation of cellular networks. Up to 100 times faster than 4G, 5G is creating never-before-seen opportunities for people and businesses. Faster connectivity speeds, ultra-low latency and greater bandwidth is advancing societies, transforming industries and dramatically enhancing day-to-day experiences. Services that we used to see as futuristic, such as e-health, connected vehicles and traffic systems and advanced mobile cloud gaming have arrived. With 5G technology, we can help create a smarter, safer and more sustainable future.


* Network performance and evolution lead for all Europe and Latin America, Ericsson Company (Poland office)

* Professional consultant for network performance and 5G evolution lead with more than 15 years of experience in different Telco topics

* Responsibilities covering all Europe and Latin America:

Spectrum & Regulatory Advisory (spectrum and bandwidth acquisition advisory to operators, also spectrum interference topics)

NSA to SA Evolutions (5G spectrum architecture and deployment strategy

5G Evolution Proof Points (NSA/SA coverage extension NR mid band link budget, ESS spectrum sharing system simulator and SA strategy)  

Performance Benchmarking (OOKLA speed test and crowdsourced data analytics)

Network Performance Assessment (app coverage, VoLTE, recommended actions)

LTE/NR Product Segmentation (capacity improvement with NR introduction, DC /NR CA coverage extension gain)

LTE Capacity Expansion & Planning (LTE densification, transport capacity, NR introduction new use cases eMBB, FWA)

MBB Coverage & Device Analysis (smart network investment, improve coverage and CA strategy based on UE cap)

NR TDD Build with Precision (360 analysis based on site build, EMF, capacity, app coverage and TCO)

Site Evolution (network inventory & dimensioning)

Hot Topics (Cloud RAN, Open RAN, private network, and NW sharing)

Planning Network Evolutions with 5G Predictions Future 3 Years


   – TOP 15 woman in 5G (perspektywy woman in tech Dec. 2020)

   – Speaker and Mentor for ‚IT FOR SHE‘ Woman Tech Camp 2021

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Applied Data Science – Use Cases and Challenges in the Semiconductor Industry

Dr. Anja Zernig | KAI Kompetenzzentrum Automobil- und Industrieelektronik GmbH Villach |
Friday, November 26, 2021 | 10:00 (CET, 09:00 UTC) | Online:

Abstract: AI has infected the world. Today, there is a huge hype around Data Science activities all over the world, where one of the biggest challenges for the industry is to deliver financial value quickly but also sustainably. In her talk, she will show some examples on latest Use Cases in the area of Data Science within the semiconductor industry, including technical approaches and practical challenges. Further, she will give some personal insights on important enabling factors that make a Data Science project successful.

Bio: Anja Zernig coordinates Data Science projects at KAI Kompetenzzentrum Automobil- und Industrieelektronik GmbH in Villach, which is a 100% subsidiary of Infineon Technologies Austria AG. Dr. Zernig studied Technical Mathematics at the University of Klagenfurt and received her PhD in 2016. Afterwards, she has been applied as a researcher at KAI, focusing on topics like outlier and anomaly detection, pattern recognition, applied statistical methods and Machine Learning techniques. Since 2019 she is coordinating a team of Data Scientists, involved in various national and international funding projects and acts as a link between the industry and academic collaboration partners. She is supervising researchers and students, dealing with innovative data-analytical concepts within the semiconductor production, testing and optimization and publishes latest scientific insights in different conference and Journal papers. Beside this, Dr. Zernig participates in and supports local Data Science activities, e.g. she is part of the organizing team of the Women in Data Science Villach. In recent times, she is focusing on deployment strategies to guarantee sustainable Machine Learning lifecycles.

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RL-Cache: Learning-Based Cache Admission for Content Delivery

Sergey Gorinsky | IMDEA Networks Institute, Madrid |
Friday, November 12, 2021 | 14:00 (CET, 13:00 UTC) | S.0.05

Content delivery networks (CDNs) distribute much of the Internet content by caching and serving the objects requested by users. A major goal of a CDN is to maximize the hit rates of its caches, thereby enabling faster content downloads to the users. Content caching involves two components: an admission algorithm to decide whether to cache an object and an eviction algorithm to decide which object to evict from the cache when it is full. In this paper, we focus on cache admission and propose an algorithm called RL-Cache that uses model-free reinforcement learning (RL) to decide whether or not to admit a requested object into the CDN’s cache. Unlike prior approaches that use a small set of criteria for decision making, RL-Cache weights a large set of features that include the object size, recency, and frequency of access. We develop a publicly available implementation of RL-Cache and perform an evaluation using production traces for the image, video, and web traffic classes from Akamai’s CDN. The evaluation shows that RL-Cache improves the hit rate in comparison with the state of the art and imposes only a modest resource overhead on the CDN servers. Further, RL-Cache is robust enough that it can be trained in one location and executed on request traces of the same or different traffic classes in other locations of the same geographic region.

Sergey Gorinsky is a tenured Research Associate Professor at IMDEA Networks Institute in Madrid, Spain. He joined the institute in 2009 and leads the NetEcon (Network Economics) research group there. Dr. Gorinsky received his Ph.D. and M.S. degrees from the University of Texas at Austin, USA in 2003 and 1999 respectively and Engineer degree from Moscow Institute of Electronic Technology, Zelenograd, Russia in 1994. From 2003 to 2009, he served on the tenure-track faculty at Washington University in St. Louis, USA. In 2010-2014, Dr. Gorinsky was a Ramón y Cajal Fellow funded by the Spanish Government. Sergey Gorinsky graduated four Ph.D. students. The areas of his primary research interests are computer networking, distributed systems, and network economics. His work appeared at top conferences and journals such as SIGCOMM, CoNEXT, INFOCOM, Transactions on Networking, and Journal on Selected Areas in Communications. He served as a TPC chair of ICNP 2017 and other conferences, as well as a TPC member for a much broader conference population. Sergey Gorinsky contributed to conference organization in many roles, such as a general chair of SIGCOMM 2018 and ICNP 2020. He also served as an evaluator of research proposals and projects for the European Research Council (ERC StG), European Commission (Horizon 2020, FP7), COST Association, and numerous other funding agencies.

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Software Developer mit Teamleader Perspektive (M/W)

IoT40 Systems GmbH baut stabile zukunftssichere IoT und AI Lösungen und führt seine
Kunden durch die digitale Transformation zum Erfolg!

„We don’t predict the future – we build it“

Im Zuge unserer Tätigkeiten im Industrie 4.0 Umfeld, haben wir uns auch auf individuelle
Softwareentwicklung spezialisiert und Lösungen ausgearbeitet, die in bestehende ITStrukturen
unserer Kunden nahtlos integriert werden können. Wir beschäftigen uns mit künstlicher Intelligenz
und setzen diese in Produktionsbereich bei Kunden ein. Wir leben unsere Leidenschaft für das Internet
der Dinge voll und ganz aus.

Weitere Informationen zur Stellenausschreibung / Bewerbung finden Sie hier:

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