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We are hiring: University Assistant Post-Doc [DL: October 10th, 2024]
by Miksch Silvia 22 Sep '24

22 Sep '24

TL;DR: University Assistant Post-Doc in the field of Visual Analytics and Visualization (focus on network visualization, time, space, and uncertainty) At the Institute of Visual Computing and Human-Centered Technology, TU Wien, in our Research Unit of Visual Analytics, is offering a position as a University Assistant Post-Doc (all genders) for 6 years for 40 hours/week. >>> Research Topics: Visual Analytics and Visualization (focus on network visualization, time, space, and uncertainty) For more information see: https://jobs.tuwien.ac.at/Job/239572 Entry-level salary is determined by the pay grade B1 of the Austrian collective agreement for university staff. This is a minimum of currently EUR 4,752.30/month gross, 14 times/year for 40 hours/week. Relevant working experiences may increase the monthly income. We look forward to receiving your application until October 10th, 2024. Any questions, please contact silvia.miksch(a)tuwien.ac.at We are looking forward to your applications ================================================================ Silvia Miksch, Univ.-Prof. Ph.D. MSc. Vienna University of Technology (TU Wien) Faculty of Informatics Institute of Visual Computing and Human-Centered Technology Favoritenstrasse 9-11 / 193-07 A-1040 Vienna, Austria, Europe email: silvia.miksch(a)tuwien.ac.at url: https://www.cvast.tuwien.ac.at/team/silvia-miksch phone: +43-1-58801-18824 phone-sec: +43-1-58801-18801 ================================================================= :::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::: B O O K 2nd edition ::: VISUALIZATION OF TIME-ORIENTED DATA Wolfgang Aigner • Silvia Miksch • Heidrun Schumann • Christian Tominski Springer, 2023 • http://www.timeviz.net :::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::: B O O K 2nd edition :::

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Antw: [EXT] We are hiring: PhD student positions within Cluster of Excellence Bilateral Artificial Intelligence (out-of-office message)
by Dorit Merhof 22 Sep '24

22 Sep '24

I am out of the office until 28th September without email contact. I'll respond to your message after my return. In case of emergency, please contact Nicole Kerscher (email: nicole.kerscher(a)ur.de) Best regards, Dorit Merhof >>> Miksch Silvia <silvia.miksch(a)tuwien.ac.at> 22.9.24 21:40 >>> TL;DR: For our research unit, we are seeking for a PhD student in the field of Visual Analytics & Dynamic Network Visualizations see https://www.vcla.at/2024/08/open-positions-cluster-of-excellence/#miksch ********************************************************* Cluster of Excellence Bilateral Artificial Intelligence ********************************************************* Call for Applications For the recently established Cluster of Excellence (CoE) Bilateral Artificial Intelligence (BILAI), funded by the Austrian Science Fund (FWF), we are looking for more than 50 PhD students and 10 Post-Doc researchers (m/f/d) to join our team at one of the six leading research institutions across Austria (see below). In BILAI, major Austrian players in Artificial Intelligence (AI) are teaming up to work towards Broad AI. As opposed to Narrow AI, which is characterized by task-specific skills, Broad AI seeks to address a wide array of problems, rather than being limited to a single task or domain. To develop its foundations, BILAI employs a Bilateral AI approach, effectively combining sub-symbolic AI (neural networks and machine learning) with symbolic AI (logic, knowledge representation, and reasoning) in various ways. Harnessing the full potential of both symbolic and sub-symbolic approaches can open new avenues for AI, enhancing its ability to solve novel problems, adapt to diverse environments, improve reasoning skills, and increase efficiency in computation and data use. These key features enable a broad range of applications for Broad AI, from drug development and medicine to planning and scheduling, autonomous traffic management, and recommendation systems. Prioritizing fairness, transparency, and explainability, the development of Broad AI is crucial for addressing ethical concerns and ensuring a positive impact on society. The research team is committed to cross-disciplinary work in order to provide theory and models for future AI and deployment to applications. CoE Research Institutions: Johannes Kepler Universität Linz (JKU Linz) Technische Universität Wien (TU Wien) Alpen-Adria-Universität Klagenfurt (AAU) Institute of Science and Technology Austria (ISTA) Technische Universität Graz (TU Graz) Wirtschaftsuniversität Wien (WU Wien) The call for applications is available at https://www.bilateral-ai.net/jobs/. For our research unit, we are seeking for a PhD student in the field of Visual Analytics & Dynamic Network Visualizations see https://www.vcla.at/2024/08/open-positions-cluster-of-excellence/#miksch _______________________________________________ FG-VIS Mailingliste -- fg-vis(a)lists.gi.de Zur Abmeldung von dieser Mailingliste senden Sie eine Nachricht an fg-vis-leave(a)lists.gi.de

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We are hiring: PhD student positions within Cluster of Excellence Bilateral Artificial Intelligence
by Miksch Silvia 22 Sep '24

22 Sep '24

TL;DR: For our research unit, we are seeking for a PhD student in the field of Visual Analytics & Dynamic Network Visualizations see https://www.vcla.at/2024/08/open-positions-cluster-of-excellence/#miksch ********************************************************* Cluster of Excellence Bilateral Artificial Intelligence ********************************************************* Call for Applications For the recently established Cluster of Excellence (CoE) Bilateral Artificial Intelligence (BILAI), funded by the Austrian Science Fund (FWF), we are looking for more than 50 PhD students and 10 Post-Doc researchers (m/f/d) to join our team at one of the six leading research institutions across Austria (see below). In BILAI, major Austrian players in Artificial Intelligence (AI) are teaming up to work towards Broad AI. As opposed to Narrow AI, which is characterized by task-specific skills, Broad AI seeks to address a wide array of problems, rather than being limited to a single task or domain. To develop its foundations, BILAI employs a Bilateral AI approach, effectively combining sub-symbolic AI (neural networks and machine learning) with symbolic AI (logic, knowledge representation, and reasoning) in various ways. Harnessing the full potential of both symbolic and sub-symbolic approaches can open new avenues for AI, enhancing its ability to solve novel problems, adapt to diverse environments, improve reasoning skills, and increase efficiency in computation and data use. These key features enable a broad range of applications for Broad AI, from drug development and medicine to planning and scheduling, autonomous traffic management, and recommendation systems. Prioritizing fairness, transparency, and explainability, the development of Broad AI is crucial for addressing ethical concerns and ensuring a positive impact on society. The research team is committed to cross-disciplinary work in order to provide theory and models for future AI and deployment to applications. CoE Research Institutions: Johannes Kepler Universität Linz (JKU Linz) Technische Universität Wien (TU Wien) Alpen-Adria-Universität Klagenfurt (AAU) Institute of Science and Technology Austria (ISTA) Technische Universität Graz (TU Graz) Wirtschaftsuniversität Wien (WU Wien) The call for applications is available at https://www.bilateral-ai.net/jobs/. For our research unit, we are seeking for a PhD student in the field of Visual Analytics & Dynamic Network Visualizations see https://www.vcla.at/2024/08/open-positions-cluster-of-excellence/#miksch

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Antw: [EXT] Meeting GI FG Visualisierung @ VMV (out-of-office message)
by Dorit Merhof 02 Sep '24

02 Sep '24

I am out of the office until 28th September without email contact. I'll respond to your message after my return. In case of emergency, please contact Nicole Kerscher (email: nicole.kerscher(a)ur.de) Best regards, Dorit Merhof >>> Johanna Schmidt <johanna.schmidt(a)vrvis.at> 2.9.24 17:33 >>> Liebe Kolleg:innen, aus dieses Jahr wird das jährliche Meeting der GI Fachgruppe Visualisierung auf der VMV stattfinden. Wir haben dafür folgenden Termin fixiert: _ Freitag, 13.9.2024, 12:00 - 13:00_ Für die lokalen Teilnehmer:innen: TU München Garching (VMV Venue), Seminarraum 02.09.023 Es wird ein Catering geben, für Mittagessen ist also gesorgt. Für die hybriden Teilnehmer:innen: Bitte in das Zoom-Meeting einwählen https://us06web.zoom.us/j/87507387268?pwd=ttdcXl5FEkdbTNPZidalRea2eRyCSI.1 _Agenda_ - Bericht über letztes Jahr - Vorstellung unserer Sammlung von VIS-Themen der vergangenen Jahre - Feedback sammeln bzw. offene Diskussion - Verschiedenes Falls ihr lieber mit eurem Kalender arbeitet, es folgt noch eine extra Termineinladung. Wir freuen uns auf Eure Teilnahme! Liebe Grüße, Johanna Schmidt Lars Linsen Daniel Weiskopf -- Dr. Johanna Schmidt Head of Visual Analytics Group VRVis Forschungs-GmbH,www.VRVis.at, FN: 195369h, HG Wien mail:johanna.schmidt@vrvis.at tel +43(0)1 9089892 302

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Precall "Bilateral AI" (50 PhDs, 10 Postdocs), fall 2024
by Miksch Silvia 26 Jun '24

26 Jun '24

Cluster of Excellence Bilateral Artificial Intelligence Pre-Call for Applications For the recently established Cluster of Excellence CoE Bilateral Artificial Intelligence (BILAI), funded by the Austrian Science Fund (FWF), we are looking for more than 50 PhD students and 10 Post-Doc researchers (m/f/d) to join our team at one of the six leading research institutions across Austria (see below). In BILAI, major Austrian players in Artificial Intelligence (AI) are teaming up to work towards Broad AI. As opposed to Narrow AI, which is characterized by task-specific skills, Broad AI seeks to address a wide array of problems, rather than being limited to a single task or domain. To develop its foundations, BILAI employs a Bilateral AI approach, effectively combining sub-symbolic AI (neural networks and machine learning) with symbolic AI (logic, knowledge representation, and reasoning) in various ways. Harnessing the full potential of both symbolic and sub-symbolic approaches can open new avenues for AI, enhancing its ability to solve novel problems, adapt to diverse environments, improve reasoning skills, and increase efficiency in computation and data use. These key features enable a broad range of applications for Broad AI, from drug development and medicine to planning and scheduling, autonomous traffic management, and recommendation systems. Prioritizing fairness, transparency, and explainability, the development of Broad AI is crucial for addressing ethical concerns and ensuring a positive impact on society. The research team is committed to cross-disciplinary work in order to provide theory and models for future AI and deployment to applications. CoE Research Institutions: Johannes Kepler Universität Linz (JKU Linz) Technische Universität Wien (TU Wien) Alpen-Adria-Universität Klagenfurt (AAU) Institute of Science and Technology Austria (ISTA) Technische Universität Graz (TU Graz) Wirtschaftsuniversität Wien (WU Wien) Board of directors: Sepp Hochreiter (JKU Linz) Agata Ciabattoni (TU Wien) Thomas Eiter (TU Wien) Gerhard Friedrich (AAU) Christoph Lampert (ISTA) Robert Legenstein (TU Graz) Axel Polleres (WU Wien) Martina Seidl (JKU Linz) The call for applications will open on *** September 1,*** 2024. For more information, see http://www.bilateral-ai.net Stay tuned

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[REMINDER - FUNDED PHD POSITION AT TU VIENNA] ViSual ANalytics for Event-based Diffusion on Networks @ TU Wien f/m/d (30-40h) DL 15/04
by Arleo, Alessio 02 Apr '24

02 Apr '24

** FUNDED PHD POSITION AT TU VIENNA - DL 15/04** [Funded PhD Position] ViSual ANalytics for Event-based Diffusion on Networks @ TU Wien f/m/d (30-40h) We are hiring a talented PhD candidate in an international project on the visual analytics of complex diffusion processes over temporal networks. You will work in the context of SANE, a project held by TU Vienna (Austria), the University of Cologne (Germany) and the University of Newcastle (United Kingdom). This project arrangement gives you unique career opportunities, including student exchanges abroad, regular meetings with researchers from the other institutions participating to the project. We are looking for a PhD candidate to work from the TU Wien, at the Institute of Visual Computing and Human-Centered Technology, in the Research Unit of Visual Analytics. We offer a position as project assistant (prae-doc) limited to 3 years for 30 hours/week (that can beex-tended to 40). Gross annual (monthly) salary of Euro 43,650 (2,464.80) according to FWF regulations. Pandemics, computer malware attacks, misinformation campaigns – all of these phenomena have one thing in common: some kind of “information” (a pathogen, a virus, fake news) that spreads across an underlying structure of elements interconnected between each other by some kind of relationship (physical contacts, public WiFi, social networks). In Computer Science, such structures are known as graphs or networks – and have been extensively researched. Within this formalization, the phenomena described above are referred to as diffusion processes, and their dynamics have been studied and researched to obtain models that allow researchers and institutions predict, mitigate, and generally understand such complex events. Visual Analytics (VA) showed its potential in communicating and investigating information diffusion processes over networks. Diffusion processes are highly dynamic and stochastic phenomena. To this end, VA needs to tackle two challenges: representing the progression on the underlying dynamic network structure and capturing the uncertainty of the process. The majority of existing VA approaches approximate the problem by imposing a discrete time structure to the input data and disregarding uncertainties. We strive to be among the first to systematically explore the design space of VA for event-based information diffusion with uncertainty. We investigate promising but currently under-represented research trends in VA with innovative solutions and approaches, to contribute to the current body of knowledge in the field and open new and exciting research questions. Wanna know more? Check the project description and complete job posting at https://www.cvast.tuwien.ac.at/projects/sane Deadline for applications: 15 April 2024 Expected start: May/June 2024 Questions? You can reach me at a.arleo(a)tue.nl

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[FUNDED PHD POSITION AT TU VIENNA] ViSual ANalytics for Event-based Diffusion on Networks @ TU Wien f/m/d (30-40h)
by alessio.arleo＠tuwien.ac.at 11 Mar '24

11 Mar '24

** FUNDED PHD POSITION AT TU VIENNA ** [Funded PhD Position] ViSual ANalytics for Event-based Diffusion on Networks @ TU Wien f/m/d (30-40h) We are hiring a talented PhD candidate in an international project on the visual analytics of complex diffusion processes over temporal networks. You will work in the context of SANE, a project held by TU Vienna (Austria), the University of Cologne (Germany) and the University of Newcastle (United Kingdom). This project arrangement gives you unique career opportunities, including student exchanges abroad, regular meetings with researchers from the other institutions participating to the project. We are looking for a PhD candidate to work from the TU Wien, at the Institute of Visual Computing and Human-Centered Technology, in the Research Unit of Visual Analytics. We offer a position as project assistant (prae-doc) limited to 3 years for 30 hours/week (that can beex-tended to 40). Gross annual (monthly) salary of Euro 43,650 (2,464.80) according to FWF regulations. Pandemics, computer malware attacks, misinformation campaigns – all of these phenomena have one thing in common: some kind of “information” (a pathogen, a virus, fake news) that spreads across an underlying structure of elements interconnected between each other by some kind of relationship (physical contacts, public WiFi, social networks). In Computer Science, such structures are known as graphs or networks – and have been extensively researched. Within this formalization, the phenomena described above are referred to as diffusion processes, and their dynamics have been studied and researched to obtain models that allow researchers and institutions predict, mitigate, and generally understand such complex events. Visual Analytics (VA) showed its potential in communicating and investigating information diffusion processes over networks. Diffusion processes are highly dynamic and stochastic phenomena. To this end, VA needs to tackle two challenges: representing the progression on the underlying dynamic network structure and capturing the uncertainty of the process. The majority of existing VA approaches approximate the problem by imposing a discrete time structure to the input data and disregarding uncertainties. We strive to be among the first to systematically explore the design space of VA for event-based information diffusion with uncertainty. We investigate promising but currently under-represented research trends in VA with innovative solutions and approaches, to contribute to the current body of knowledge in the field and open new and exciting research questions. Wanna know more? Check the project description and complete job posting at https://www.cvast.tuwien.ac.at/projects/sane Deadline for applications: 15 April 2024 Expected start: May/June 2024 Questions? You can reach me at a.arleo(a)tue.nl

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*DEADLINE EXTENSION* [DL: March 8th] 1st Workshop on Visual Process Analytics (VIPRA 2024) - Co-Located EuroVis 2024
by Arleo, Alessio 29 Feb '24

29 Feb '24

##################################################### ** DEADLINE EXTENSION ** 1st Workshop on Visual Process Analytics (VIPRA 2024) https://vipra-workshop.github.io/homepage/ ##################################################### Call for Papers ---------------- The 2024 Workshop on Visual Process Analytics (Vipra 2024) is the 1st annual workshop that provides an opportunity for participants to discuss state-of-the-art event visualization and process mining and learn how both techniques can mutually benefit each other. In this workshop we aim to bring together researchers from both Visual Analytics and Process Mining, to learn, create a common language, spark collaborations, and advance both fields. Process Mining is a data-driven discipline that focuses on extracting valuable insights and knowledge from the vast amount of event data generated within organizations. It involves the discovery, analysis, and improvement of process models by leveraging techniques from data science, machine learning, and data analytics. By examining event logs and historical data, process mining aims to provide a clear, objective view of how processes actually operate, identifying bottlenecks, deviations, and opportunities for optimization. In turn, this supports organizations in making more data-informed decisions, and enhance operational efficiency. Visual analytics is an interdisciplinary field that combines data visualization, analytical techniques, and interactive interfaces to help people make sense of complex and often large datasets. It focuses on using the power of visual representations to facilitate data exploration, pattern recognition, and decision-making. By enabling users to interact with data through graphical interfaces, visual analytics promotes a deeper understanding of information, uncovers hidden insights, and supports the detection of trends, anomalies, and relationships that might be challenging to discern through traditional data analysis methods alone. We believe that process mining and visual analytics can mutually benefit each other. Until now, there has been limited interaction between the two fields, despite their shared objectives. In process mining, the emphasis has traditionally been on data extraction and process discovery, often neglecting the visualization aspect, whereas in visual analytics, event visualization efforts have often overlooked the wealth of process mining techniques that can enhance visualizations and analysis. Bridging this gap and fostering collaboration between the two fields will not only lead to more informed, efficient, and data-driven improvements in various domains but also advance the research and innovation in both process mining and visual analytics, ultimately contributing to a more holistic and integrated approach to understanding and optimizing complex processes. Topics and Scope ---------------- We encourage submissions from all areas on the intersection of visual analytics and process mining. The 2024 Workshop on Visual Process Analytics will cover a range of topics exploring the intersection of PM and VA including (but not limited to): - VA to support exploration and comparison of discovered process models - VA to support the comparison of event logs and process models, e.g., in conformance checking - VA to support the generation of hypotheses about the business process and the identification of process improvement ideas - VA to represent temporal and spatial uncertainty in PM models - Research challenges, pitfalls, and opportunities related to the intersection of VA and PM - The role of AI in PM, and how Visualization and explainable-AI solutions can support model comprehension, exploration, and refinement Paper Submission, Publication, and Format Guidelines -------------------------------------- We will accept short papers with up to 5 pages plus one page of references (5 + 1). We encourage a diverse range of submissions and demonstrations in any of the short paper types listed below: - Application/Case-study papers (format: 5 + 1) - Research papers (format: 5 + 1) - Position papers and system demos papers (format: 2 + 1) All material will be submitted via the Precision Conference System (PCS): https://new.precisionconference.com/submissions For all submissions, please use the EuroVis 2024 LateX2e Style. On Overleaf, this template can be imported easily by selecting "New Project" -> "Upload Project" and uploading the zip file. Use EGAuthorGuidelines-eurovis24-short.tex for your submission: https://medialib.cmcdn.dk/medialibrary/80234312-1C55-406C-86A7-410099EB2739… Accepted papers will be included in the EuroGraphics Digital Library and will be assigned a DOI as fully citable publications. Contact vipra.workshop at gmail.com if you have any questions or visit https://vipra-workshop.github.io/homepage/ Important Dates --------------- - Deadline for submission: March 8th, 2024 !! EXTENDED !! - Notification of acceptance: April 12th, 2024 - Camera-ready papers due: April 26th, 2024 - Vipra 2024 Workshop: May 27th, 2024 (in conjunction with EuroVis 2024) All submission deadlines are at 23:59 GMT on the date indicated. Organizers ---------- Alessio Arleo, Eindhoven University of Technology Stef van den Elzen, Eindhoven University of Technology Tatiana von Landesberger, Universität Köln Luise Pufahl, TU München - Heilbronn Jana-Rebecca Rehse, Universität Mannheim Francesca Zerbato, Universität St. Gallen

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*LAST CALL* [DL: March 1st] 1st Workshop on Visual Process Analytics (VIPRA 2024) - Co-Located EuroVis 2024
by alessio.arleo＠tuwien.ac.at 12 Feb '24

12 Feb '24

##################################################### ** FINAL CALL ** 1st Workshop on Visual Process Analytics (VIPRA 2024) https://vipra-workshop.github.io/homepage/ ##################################################### Call for Papers ---------------- The 2024 Workshop on Visual Process Analytics (Vipra 2024) is the 1st annual workshop that provides an opportunity for participants to discuss state-of-the-art event visualization and process mining and learn how both techniques can mutually benefit each other. In this workshop we aim to bring together researchers from both Visual Analytics and Process Mining, to learn, create a common language, spark collaborations, and advance both fields. Process Mining is a data-driven discipline that focuses on extracting valuable insights and knowledge from the vast amount of event data generated within organizations. It involves the discovery, analysis, and improvement of process models by leveraging techniques from data science, machine learning, and data analytics. By examining event logs and historical data, process mining aims to provide a clear, objective view of how processes actually operate, identifying bottlenecks, deviations, and opportunities for optimization. In turn, this supports organizations in making more data-informed decisions, and enhance operational efficiency. Visual analytics is an interdisciplinary field that combines data visualization, analytical techniques, and interactive interfaces to help people make sense of complex and often large datasets. It focuses on using the power of visual representations to facilitate data exploration, pattern recognition, and decision-making. By enabling users to interact with data through graphical interfaces, visual analytics promotes a deeper understanding of information, uncovers hidden insights, and supports the detection of trends, anomalies, and relationships that might be challenging to discern through traditional data analysis methods alone. We believe that process mining and visual analytics can mutually benefit each other. Until now, there has been limited interaction between the two fields, despite their shared objectives. In process mining, the emphasis has traditionally been on data extraction and process discovery, often neglecting the visualization aspect, whereas in visual analytics, event visualization efforts have often overlooked the wealth of process mining techniques that can enhance visualizations and analysis. Bridging this gap and fostering collaboration between the two fields will not only lead to more informed, efficient, and data-driven improvements in various domains but also advance the research and innovation in both process mining and visual analytics, ultimately contributing to a more holistic and integrated approach to understanding and optimizing complex processes. Topics and Scope ---------------- We encourage submissions from all areas on the intersection of visual analytics and process mining. The 2024 Workshop on Visual Process Analytics will cover a range of topics exploring the intersection of PM and VA including (but not limited to): - VA to support exploration and comparison of discovered process models - VA to support the comparison of event logs and process models, e.g., in conformance checking - VA to support the generation of hypotheses about the business process and the identification of process improvement ideas - VA to represent temporal and spatial uncertainty in PM models - Research challenges, pitfalls, and opportunities related to the intersection of VA and PM - The role of AI in PM, and how Visualization and explainable-AI solutions can support model comprehension, exploration, and refinement Paper Submission, Publication, and Format Guidelines -------------------------------------- We will accept short papers with up to 5 pages plus one page of references (5 + 1). We encourage a diverse range of submissions and demonstrations in any of the short paper types listed below: - Application/Case-study papers (format: 5 + 1) - Research papers (format: 5 + 1) - Position papers and system demos papers (format: 2 + 1) All material will be submitted via the Precision Conference System (PCS): https://new.precisionconference.com/submissions For all submissions, please use the EuroVis 2024 LateX2e Style. On Overleaf, this template can be imported easily by selecting "New Project" -> "Upload Project" and uploading the zip file. Use EGAuthorGuidelines-eurovis24-short.tex for your submission: https://medialib.cmcdn.dk/medialibrary/80234312-1C55-406C-86A7-410099EB2739… Accepted papers will be included in the EuroGraphics Digital Library and will be assigned a DOI as fully citable publications. Contact vipra.workshop at gmail.com if you have any questions or visit https://vipra-workshop.github.io/homepage/ Important Dates --------------- - Deadline for submission: March 1st, 2024 - Notification of acceptance: April 12th, 2024 - Camera-ready papers due: April 26th, 2024 - Vipra 2024 Workshop: May 27th, 2024 (in conjunction with EuroVis 2024) All submission deadlines are at 23:59 GMT on the date indicated. Organizers ---------- Alessio Arleo, Eindhoven University of Technology Stef van den Elzen, Eindhoven University of Technology Tatiana von Landesberger, Universität Köln Luise Pufahl, TU München - Heilbronn Jana-Rebecca Rehse, Universität Mannheim Francesca Zerbato, Universität St. Gallen

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Last Reminder >>> Open Position as Head of the Junior Research Group "Human-centered AI" at CAIMed, PLRI, Hannover Medical School, Germany
by Oeltze-Jafra.Steffen＠mh-hannover.de 29 Jan '24

29 Jan '24

**************************************************************************************** Apply until January, 31st for a Group Leader Position in Visual Analytics&AI **************************************************************************************** Hannover Medical School (MHH) is one of the locations of the newly established Lower Saxony research center for artificial intelligence and causal methods in medicine - CAIMed<https://caimed.de/en/>. At CAIMed, international teams research and develop innovative methods at the interface between AI and personalized medicine and contribute to tackling common diseases such as cancer, cardiovascular diseases and infections. CAIMed at MHH offers a position as Head of the Junior Research Group "Human-centered Al"<https://caimed.de/en/forschung/ki-entscheidungen/menschzentrierte-ki/> to be filled at the earliest possible date. If you are keen on establishing a research team (head + 2 PhDs + 1/2 secretary) that works on blending visual analytics and AI for the analysis of medical (imaging) data, apply until January, 31st through the job portal of MHH: EN: https://mhh.hr4you.org/job/view/2144/head-f-d-m-of-the-caimed-junior-resear… DE: https://mhh.hr4you.org/job/view/2142/leitung-w-d-m-der-caimed-nachwuchsfors… The research group will be associated with the Peter L. Reichertz Institute for Medical Informatics (PLRI) of TU Braunschweig and MHH. PLRI, one of the largest medical informatics institutions in Germany, offers excellent research opportunities. We collaborate in interdisciplinary projects with health care centers, research institutes, enterprises and public organizations on a regional, national and global level. Your tasks * Development and establishment of the junior research group * Developing new human-centered AI approaches together with clinical partners * Support in the acquisition of third-party funding * Publication of high-quality scientific publications * Involvement in national and international research networks * Collaboration in university teaching and supervision of students more details: CAIMed. <https://caimed.de/en/forschung/ki-entscheidungen/menschzentrierte-ki/> Our requirements * PhD in computer science, data science, business, medical, bio- or business informatics, mathematics or a comparable degree program * Extensive research experience in (at least) one of the following areas * Visual analytics of medical data * AI-based medical image analysis * Clinical decision support systems * Medical data science and/or knowledge engineering * Above-average publication profile * Very good oral and written English skills * Willingness to work closely with an interdisciplinary project team * Initiative and a results-orientated working style * Experience in acquiring third-party funding and management are an advantage We offer * a full-time position with 38.5 hours per week, initially limited until 31 October 2028, with one of the largest employers in the state of Lower Saxony * remuneration in accordance with TV-L in EG 14-15, taking into account the individual qualification, with the benefits of the public sector (e.g. company pension scheme and additional insurance through VBL) * increased flexibility through partially mobile work or teleworking * the opportunity to acquire your own third-party funding and to further develop your own academic qualifications (habilitation) and leadership skills * Work in a motivated, team-orientated and international research institute * Personal and professional development opportunities - supported by our diverse internal and external training and further education programs * a job ticket for local public transport and other diverse offers * an excellent range of sports, counselling and prevention programs - because your health is important to us -- Prof. Dr.-Ing. Steffen Oeltze-Jafra Peter L. Reichertz Institute for Medical Informatics of TU Braunschweig and Hannover Medical School Medical Information Systems, Campus Hannover OE 8420, room: M02-S0-0190, Karl-Wiechert-Allee 3, 30625 Hannover, Germany tel: +49 511 532-80830, fax: +49 511 532-5297 oeltze-jafra.steffen(a)mh-hannover.de<mailto:oeltze-jafra.steffen@mh-hannover.de> [www.plri.de]www.plri.de<http://www.plri.de>

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