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The University of Vienna is internationally renowned for its excellence in teaching and research, and counts more than 7,500 academics from all disciplines. This breadth of expertise offers unique opportunities to address the complex challenges of modern society, to develop comprehensive new approaches, and educate the problem-solvers of tomorrow from a multidisciplinary perspective.

At the Faculty of Business, Economics and Statistics, the University of Vienna seeks to appoint a

Tenure-Track Professor in the field ofFoundations of Machine Learning in Finance

The position:

We are looking for scientists with demonstrated excellence in machine learning approaches to financial problems combined with their mathematical, probabilistic and statistical foundations. This includes (but is not limited to) the analysis of deep (reinforcement) learning algorithms, signature methods, optimal control and game theory as well as their applications to various problems in banking, finance and economics, such as risk management and hedging, financial time-series prediction and market generation, model calibration or market making. The position will contribute on one hand to the mathematical foundations of data science and on the other hand will focus on financial and economic applications that can be solved by novel machine learning algorithms. In this respect, the Vienna scientific landscape offers many cooperation opportunities, in particular within the Research Network Data Science @ Uni Vienna, a platform for fundamental research in data science and practical applications based on data science techniques.

Your academic profile:

We expect the successful candidate to acquire, within three years, proficiency in German sufficient for teaching in bachelors programmes and for participation in university committees.

We offer:

Application documents:

If you have any questions, please contact:

tenuretrack.personal@univie.ac.at

We look forward to new personalities in our team!

The University of Vienna has an anti-discriminatory employment policy and attaches great importance to equal opportunities, the advancement of women and diversity. We lay special emphasis on increasing the number of women in senior and in academic positions among the academic and general university staff and therefore expressly encourage qualified women to apply. Given equal qualifications, preference will be given to female candidates.

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Reference no.: TT0623Wiwi01

Application deadline: 15 September 2023

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Professor in the Field of Foundations of Machine Learning in ... - Times Higher Education

What the AI and Machine Learning on Microsoft Azure Advanced Specialization Means for Wipro and Its Customers

Partners like Wipro with the AI and Machine Learning on Microsoft Azure Advanced Specialization have the tools and knowledge necessary to develop AI solutions per customers requirements, build AI into their mission-critical applications and put responsible AI into action.

Achieving the AI and Machine Learning in Microsoft Azure Specialization is a proud moment for us, showcasing our deep expertise through third-party audit validation, said Don McCormick, Vice President and Head of the Wipro-Microsoft Partnership. It also highlights our commitment to foster a strong partnership with Microsoft, utilizing our solutions and accelerators built with Microsoft technologies to empower our clients to fully realize the benefits of AI and machine learning. This is our fourteenth Microsoft Advanced Specialization and we are honored to be recognized for our partnership with Microsoft. We look forward to continuing to work together to drive innovation for all our customers.

Learn more about Wipros partnership with Microsoft Azure.

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Wipro Earns Advanced Specialization in AI and Machine Learning ... - Wipro

How do we make autonomous cars safer?

That question, which is critical as self-driving cars are increasingly found on American roads, is just one that NAU researcher Truong Nghiem hopes to answer with a new project that looks at ways to integrate machine learning and physical principles into large-scale cyber-physical systems.

Nghiem, an assistant professor in the School of Informatics, Computing, and Cyber Systems, received an NSF CAREER grant for this project, which aims to develop a comprehensive and flexible framework for effective and efficient machine learning with physical constraints, which can fundamentally change how we apply machine learning to complex systems like smart energy systems, industrial automation systems and autonomous robots and cars. The CAREER award is the National Science Foundations most prestigious award for early-career faculty.

A critical challenge is how to guarantee the performance and safety of these systems, as they are typically performance- and/or safety-critical, where any failure could have devastating consequences, Nghiem said. Our approach is to tightly integrate machine learning and physical principles. The framework developed in this project will be a foundation for such an integration and will be a stepping stone toward solving the challenge. It will help make future autonomous cyber-physical systems reliable and safe.

A cyber-physical system (CPS) is an engineered system that is built from, and depends on, seamless integration of computational and physical components. They are the foundation of many modern engineering systems that make up our daily life, including cars, robots, medical devices, power grids and more, and they are becoming even more common as our lives become more automated.

Many of these systems employ machine learning and, increasingly, artificial intelligence. However, machine learning, which isnt always informed by physics, doesnt always provide the best way to teach these systems. Nghiems research focuses on physics-informed machine learning (PIML), which is capable of developing methods that seamlessly embed knowledge of a physical system into machine learning, leading to robust, accurate and consistent models.

In autonomous cars, rovers, drones and similar systems, that means fewer system errors and a safer experience for the vehicle and nearby people. However, current PIML methods are functionally too small to meet those needs.

Enter composite physics-informed machine learning, or CPIML. Nghiems project aims to advance the data-driven learning of complex, large-scale systems by synthesizing many PIML and physical component modelsits the physics equivalent of LEGO blocks that can be put together to build much larger, more complex models, with each block being an already-developed model or piece of machine learning.

This groundbreaking solution will require integrating the cyber world (machine learning, AI and computing) and the physical world (dynamic and control systems) in engineered systems, so that each world is aware of and can integrate with the other. The result will be a safer world through which people move.

Smart and autonomous cyber-physical systems will tremendously impact our lives in the near future, Nghiem said. Our productivity will substantially increase with autonomous helper robots, advanced industrial automation (Industry 4.0) and many autonomous systems in our work and personal life. Our energy infrastructures will be more efficient and reliable, and our transportation will be safer and faster. These all depend on modern technologies, including cyber-physical systems and recent advancements in machine learning and AI.

Nghiems research will also offer valuable opportunities for graduate and undergraduate students to engage in software development and real-world applications.

Heidi Toth | NAU Communications (928) 523-8737 | heidi.toth@nau.edu

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How NAU is making self-driving cars safer and smarter The NAU ... - NAU News

Machine learning startup Tecton has entered into a strategic partnership with Google Cloud to make its Tecton Feature Platform available to Google Cloud users. The platform automates the process of collecting, preparing, managing, and updating high-quality data required for training machine learning models. It ensures that the models have access to real-time predictive and generative AI applications. Tectons partnership with Google Cloud will help solution providers speed up the development of machine learning models while keeping costs under control.

Tecton was founded in 2019 by the developers behind Ubers Michelangelo machine learning platform. The company has raised $160 million through multiple funding rounds. Its platform is used for various applications, such as pricing, customer scoring, recommendation engines, automated loan processing, and fraud detection systems. These applications involve making complex decisions at scale and with high reliability. Tectons platform automates the process of creating machine learning features that power these models.

Google Cloud offers its Vertex AI system for training and deploying machine learning models and customizing large language models. Its data processing infrastructure services like DataProc and BigQuery are also commonly used in machine learning projects. The Tecton platform serves as a connective fabric, integrating these systems to build production-ready ML features. It automates the entire ML feature lifecycle, from definition and data transformation to online serving and operational monitoring.

Using the Tecton platform helps developers build better machine learning models by leveraging high-quality data. By automating data transformation and management, ML systems can be deployed into production faster. The platform also provides enterprise management and collaboration features that are often missing in ML initiatives.

Solution providers and strategic service providers performing AI and machine learning development work can use the Tecton-Google Cloud combination to work more efficiently. This partnership offers advanced machine learning feature engineering capabilities and accelerates the building of machine learning applications. It provides solution providers with another option to help their customers succeed in their ML initiatives.

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Tecton Partners with Google Cloud to Accelerate Machine Learning ... - Fagen wasanni

Maximizing Efficiency in Brewery Wastewater Treatment through Machine Learning

In recent years, the brewing industry has been grappling with the challenge of wastewater management, a critical issue that has significant implications for both the environment and the cost of production. However, the advent of machine learning technology is proving to be a game-changer, transforming the way breweries handle wastewater treatment and significantly enhancing efficiency.

Traditionally, breweries have relied on manual monitoring and control systems to manage their wastewater treatment processes. This approach is not only labor-intensive but also prone to human error. Moreover, it often fails to optimally utilize resources, leading to unnecessary waste and increased operational costs.

Enter machine learning, a subset of artificial intelligence that enables computers to learn from data and make predictions or decisions without being explicitly programmed. In the context of brewery wastewater treatment, machine learning algorithms can analyze vast amounts of data from the brewing process, identify patterns and trends, and use this information to optimize the treatment process.

One of the key ways machine learning is enhancing efficiency in brewery wastewater treatment is through predictive analytics. By analyzing historical data, machine learning models can predict future outcomes with remarkable accuracy. For instance, they can forecast the amount of wastewater that will be produced in a given period, allowing breweries to plan their treatment processes more effectively. This not only reduces the risk of overloading the treatment system but also helps breweries save on treatment costs.

Furthermore, machine learning can optimize the use of treatment chemicals. By analyzing data on the composition of the wastewater and the effectiveness of different treatment methods, machine learning models can determine the optimal amount of chemicals to use. This not only minimizes chemical waste but also ensures that the treated water meets environmental standards.

Another significant benefit of machine learning in brewery wastewater treatment is its ability to detect anomalies. By continuously monitoring the treatment process, machine learning algorithms can identify deviations from the norm, such as sudden changes in the composition of the wastewater or malfunctions in the treatment equipment. This allows breweries to address issues promptly, preventing costly disruptions and ensuring the consistency of the treatment process.

Moreover, machine learning can facilitate the reuse of wastewater in breweries. By analyzing data on the quality of the treated water, machine learning models can determine if it is suitable for reuse in non-critical processes, such as cleaning or cooling. This not only conserves water but also reduces the brewerys water footprint.

In conclusion, machine learning is revolutionizing brewery wastewater treatment, driving efficiency in multiple ways. From predictive analytics and chemical optimization to anomaly detection and water reuse, this cutting-edge technology is enabling breweries to manage their wastewater more effectively and sustainably. As machine learning technology continues to evolve, its impact on brewery wastewater treatment is likely to grow, offering even more opportunities for efficiency and sustainability.

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How Machine Learning is Improving Efficiency in Brewery ... - EnergyPortal.eu