FWF Explainable AI project P-32 554-N successfully started

This basic research project will contribute novel results, algorithms and tools to the international ai and machine learning community

Successfully started the H2020 openQKD project

This project will create an openQKD testbed for quantum communication which is highly relevant for future AI and machine learning

Project Feature Cloud – Pre-Project Meeting and Workshop successful

From October, 21-22, 2018, the project partners of the EU RIA 826078 FeatureCloud project (EUR 4,646,000,00) met at the Technische Universität München, Campus Weihenstephan.  Starting from January, 1, 2019 the project partners will work jointly for 60 months on awesome topics around federated machine learning and explainability. The project’s ground-breaking novel cloud-AI infrastructure will only exchange learned representations (the feature parameters theta θ, hence the name “feature cloud”) which are anonymous by default. This approach is privacy by design or to be more precise: privacy by architecture. The highly interdisciplinary consortium, ranging from AI and machine learning experts to medical professionals covers all aspects of the value chain: assessment of cyber risks, legal considerations and international policies, development of state-of-the-art federated machine learning technology coupled to blockchaining and encompasing social issues and AI-ethics.

Federated Machine Learning – Privacy by Design won

Federated machine learning – privacy by design EU-project granted!

Good news from Brussels: Our EU RIA project application 826078 FeatureCloud with a total volume of EUR 4,646,000,00 has just been granted. The project was submitted to the H2020-SC1-FA-DTS-2018-2020 call “Trusted digital solutions and Cybersecurity in Health and Care”. The lead is done by TU Munich and we are excited to work in a super cool project consortium together with our partners for the next 60 months. The project’s ground-breaking novel cloud-AI infrastructure only exchanges learned representations (the feature parameters theta θ, hence the name “feature cloud”) which are anonymous by default (no hassle with “real medical data” – no ethical issues). Collectively, our highly interdisciplinary consortium from AI and machine learning to medicine covers all aspects of the value chain: assessment of cyber risks, legal considerations and international policies, development of state-of-the.-art federated machine learning technology coupled to blockchaining and encompasing AI-ethics research. FeatureCloud’s goals are challenging bold, obviously, but achievable, and paving the way for a socially agreeable big data era for the benefit of future medicine. Congratulations to the great project consortium!

Open PhD machine learning

PhD position in “Biomedical data sciences and machine learning” + 2 open MSc positions
in the context of the new competence center for biomarker discovery cbmed.org located at the Medical University Graz.

… have a MSc related to Information & Computer Science (e.g. Informatics, Software Engineering, Telematics, Mathematics, …)
… are eligible to enroll in the Doctoral School Computer Science at Graz University of Technology
… are interested to work within the human-centered.ai group embedded in the international research community
… have experiences and interest in scientific work in the international context
… have a high interest in the topics data science and machine learning
… like undertaking theoretical, algorithmical, and experimental machine learning studies
… want to understand the problem of knowledge discovery from complex high-dimensional data sets

… are offering a PhD position (30 hours per week, 2100 Euro gross per month, 14 x, FWF salary) available immediately
(no closing date, the position will be filled when the ideal candidate has been found)
… a contract for four years, with opportunities to further develop into a PostDoc position with another four years
… do research in information integration in the life sciences, particularly in the integration of multiple heterogeneous data sources (e.g., -omics data, text data, image data, etc.) constituting the foundation for further machine learning based data analytics for biomarker discovery. Selected topics you have to deal with at the beginning include the research of how to integrate and analyse available data sources in the biomedical domain, a common representation and information fusion model of heterogeneous data sets and to develop and test model-based infrastructures for information integration and fusion
… are offering a workplace within the vibrant, beautiful and student friendly city of Graz in charming Austria

you are interested and motivated, please prepare
… a) your scientific résumé,
… b) a sample paper, and
… c) a research statement about your targeted scientific work within the four years (a PhD proposal)
by using the templates which you find here

and send it in one single pdf file directly to a.holzinger@human-centered.ai

We are looking forward to welcome you in our group!

Open Postdoc Position in interactive Machine Learning with complex biomedical data

A postdoc position in “knowledge discovery and interactive machine learning with complex biomedical data sets” is available immediately at the Holzinger Group (human-centered.ai) in Graz, Austria. The postdoc will be financed for four years, with an option to continue for another four years by the newly formed CBmed – Center for Biomarker Discovery and supported by the PhD school “Biomarker discovery”, which is starting with October, 1, 2015.

The challenge: Worldwide there is raising interest in biomarker discovery as an important step towards P4-medicine. The data results from various sources in different structural dimensions, and a systematic and comprehensive exploration of all these data provides a mechanism for data driven hypotheses generation. A grand challenge is to make sense of this complex data sets by applying machine learning algorithms based on the “human-in-the-loop” concept, which is of emerging interest for the international research community.

The applicant should:
1) hold a PhD in machine learning, data mining, knowledge discovery or related area of modern data science;
2) have a strong research record, documented by publications at first-tier related conferences and journals;
3) having interest in advanced methodological approaches and enjoy working in a young research group following the motto
“Science is to test crazy ideas, engineering is to bring these ideas into Business”

The successful candidate shall take an active role in the further development of our research group. Communication skills and fluency in English are required.
Conditions of employment: This post-doctoral position is provided for four years with an option for another four years. The starting date is flexible; there is no fixed deadline, so applications will be considered until the position is filled with the optimal candidate.

Application procedure: Formal applications should include:
1) A scientific curriculum vitae, including a full list of publications;
2) A statement of research interests with an outlook for the coming 4 (8) years;
3) Contact details of three reference persons.

Apply by sending your application as one single PDF document, indicating Postdoc HCI-KDD in the header directly to
Prof.Dr. Andreas HOLZINGER via e-Mail: a.holzinger@human-centered.ai

About the group: The Holzinger Group works consistently on a synergistic combination of methodologies and approaches of two areas that offer ideal conditions towards unraveling these problems: Human-Computer Interaction (HCI) and Knowledge Discovery/Data Mining (KDD), with the goal of supporting human intelligence with machine learning – human-in-the-loop – to discover novel, previously unknown insights into the data.
For more details please refer to: https://human-centered.ai/about-us

Note: The language both of the Holzinger group and the language of the PhD school is English.

Keywords: interactive machine learning, knowledge discovery, data mining, human-in-the-loop, biomedical informatics