Categories
Bridge project

Business Transformation and Organisational AI-based Decision Making

Project type: Bridge Project

Business Transformation and Organisational AI-based Decision Making

Business processes in private companies and public organisations are today widely supported by Enterprise Resource Planning, Business Process Management  and Electronic Case Management systems, put into use with the aim to improve efficiency of the business processes.

The combined result is however often an increasingly elaborate information systems landscape, leading to ineffectiveness, limited understanding of business processes, inability to predict and find the root cause of losses, errors and fraud, and inability to adapt the business processes. This lack of understanding, agility and control over business processes places a major burden on the organisations. For instance, a recent report concludes that the Danish Ministry of Taxation’s control of the state’s annual revenue of one trillion DKK is so “deficient and weak” that there is a clear “increased risk” that employees can cheat and abuse for their own gain in the same style as the recent Britta Nielsen and Armed forces cases.

Enterprise systems generate a plethora of highly granular data recording their operation. Machine learning has a great potential to aid in the analysis of this data in order to predict errors, detect fraud and improve their efficiency. Knowledge of business processes can also be used to support the needed transformation of old and heterogeneous it landscapes to new platforms. Application areas include Anti-Money-Laundering (AML) and Know-Your-Customer (KYC) supervision of business processes in the financial sector, supply chain management in agriculture and foodstuff supply, and compliance and optimisation of workflow processes in the public sector.

The research aim of the project to develop methods and tools that enable industry to develop new efficient solutions for exploiting the huge amount of business data generated by enterprise systems, with specific focus on tools and responsible methods for the use of process insights for business intelligence and transformation. Through field studies in organizations that are using AI, BPM and process mining techniques it will be investigated how organizations implement, use and create value (both operational and strategic) through AI, BPM and process mining techniques. In particular, the project will focus on how organizational decision-making changes with the implementation of AI-based algorithms in terms of decision making skills (intuitive + analytical) of the decision makers, their roles and responsibilities, their decision rights and authority and the decision context.
The scientific value of the project is new methods and user interfaces for decision support and business transformation and associated knowledge of their performance and properties in case studies. These are important contributions to provide excellent knowledge to Danish companies and education programs within AI for business innovation and processes. For capacity building the value of the project is to educate 1 industrial PhD in close collaboration between CBS, DIKU and the industrial partner DCR Solutions. The project will also provide on-line course material that can be used in existing and new courses for industry, MSc and PhD. For the business and societal value, the project has very broad applicability, targeting improvements in terms of effectiveness and control of process aware information systems across the private and public sector. Concretely, the project considers cases of customers of the participating industry partners within the financial sector, the public sector and within energy and building management. All sectors that have vital societal role. The industry partner will create business value of estimated 10-20MDkr increased turnaround and 2-3 new employees in 5-7 years through the generation of IP by the industrial researcher and the development of state- of-the-art proprietary process analysis and decision support tools.

July 1, 2021 – December 31, 2025 – 3,5 years

Total budget DKK 16,77 million / DIREC investment DKK4,95 million

Participants

Project Manager

Arisa Shollo

Associate Professor

Copenhagen Business School
Department of Digitalization

E: ash.digi@cbs.dk

Thomas Hildebrandt

Professor

University of Copenhagen
Department of Computer Science

Raghava Mukkamala

Associate Professor

Copenhagen Business School
Department of Digitalization

Morten Marquard

Founder & CEO

DCR Solutions

Søren Debois

CTO

DCR Solutions

Partners

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Bridge project

AI and Blockchains for Complex Business Processes

Project type: Bridge Project

AI and Blockchains for Complex Business Processes

Business processes in private companies and public organisations are today widely supported by Enterprise Resource Planning (ERP), Business Process Management (BPM) and Electronic Case Management (ECM) systems [1], put into use with the aim to improve efficiency of the business processes. Recently, also blockchain technologies are being proposed as a means to provide guarantees for security, computational integrity and pseudonymous agency.

The combined result is however often an increasingly elaborate information systems landscape, leading to ineffectiveness, limited understanding of business processes, inability to predict and find the root cause of losses, errors and fraud, and inability to adapt the business processes [1]. This lack of understanding, agility and control over business processes places a major burden on the organisations. For instance, a recent report concludes that the Danish Ministry of Taxation’s control of the state’s annual revenue of one trillion DKK is so “deficient and weak” that there is a clear “increased risk” that employees can cheat and abuse for their own gain in the same style as the recent Britta Nielsen and Armed forces cases.

Enterprise and block chain systems generate a plethora of highly granular data recording their operation. Machine learning has a great potential to aid in the analysis of this data in order to predict errors, detect fraud and improve their efficiency. Knowledge of business processes can also be used to support the needed transformation of old and heterogeneous it landscapes to new platforms. Application areas include Anti-Money-Laundering (AML) and Know-Your-Customer (KYC) supervision of business processes in the financial sector, supply chain management in agriculture and foodstuff supply, and compliance and optimisation of workflow processes in the public sector.

The research aim of the AI and Blockchain for Complex Business Processes project is methods and tools that enable industry to develop new efficient solutions for exploiting the huge amount of business data generated by enterprise and blockchain systems, from techniques for automatic identification of business events, via the development of new rule based process mining technologies to tools for the use of process insights for business intelligence and transformation. The project will do this through a unique bridge between industry and academia, involving two innovative, complementary industrial partners and researchers across disciplines of AI, software engineering and business intelligence from three DIREC partner universities. Open source release (under the LGPL 3.0 license) of the rule-based mining algorithms developed by the PhD assigned task 2 will ensure future enhancement and development by the research community, while simultaneously providing businesses the opportunity to include them in proprietary software.
The scientific value of the project is new methods and tools for process mining, decision support and business transformation and associated knowledge of their performance and properties in case studies. These are important contributions to provide excellent knowledge to Danish companies and education programs within AI and Blockchain technology for business innovation and processes. For capacity building the value of the project is to educate 2 PhD and 1 industrial Post Doc in close collaboration with industry. Open source availability of general project outcomes and industry collaboration enable several exploitation paths. The project will also provide on-line course material for existing and new courses for industry, MSc and PhD. For the business and societal value, the project has very broad applicability, targeting improvements in terms of effectiveness and control of process aware information systems across the private and public sector. Concretely, the project considers cases of customers of the participating industry partners within the financial sector, the public sector and within the operations and supply chains for agriculture and foodstuffs supply. All sectors that have vital societal role. The industrial partners will create business value of estimated 155MDkr increased turnaround and 10-12 new employees in 5-7 years through the generation of IP by the two industrial researchers and the development of state- of-the-art proprietary process analysis and decision support tools.

July 1, 2021 – December 31, 2025 – 3,5 years

Participants

Project Manager

Tijs Slaats

Associate Professor

University of Copenhagen
Department of Computer Science

E: slaats@di.ku.dk

Jakob Grue Simonsen

Professor

University of Copenhagen
Department of Computer Science

Thomas Hildebrandt

Professor

University of Copenhagen
Department of Computer Science

Michel Avital

Professor

Copenhagen Business School
Department of Digitalization

Henrik Axelsen

PHD Fellow

University of Copenhagen
Department of Computer Science

Christoffer Olling Back

Industry Postdoc

Gekkobrain

Hugo López

Assistant Professor

University of Copenhagen
Department of Computer Science

Søren Debois

Associate Professor

IT University of Copenhagen
Department of Computer Science

Jens Strandbygaard

CEO and Cofounder

Gekkobrain

Omri Ross

Chief Blockchain Scientist

eToro

Axel Fjelrad Christfort

PhD Fellow

University of Copenhagen
Dept. of Computer Science

Partners

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Bridge project

Mobility Analytics using Sparse Mobility Data and Open Spatial Data

Project type: Bridge Project

Mobility Analytics using Sparse Mobility Data and Open Spatial Data

The mobility of people and things is an important societal process that facilitates and affects the lives of most people. Thus, society, including industry, has a substantial interest in well-functioning outdoor and indoor mobility infrastructures that are efficient, predictable, environmentally friendly, and safe. For outdoor mobility, reduction of congestion is high on the political agenda – it is estimated that congestion costs Denmark 30 billion DKK per year. Similarly, the reduction of CO2 emissions from transportation is on the political agenda, as the transportation sector is the second largest in terms of greenhouse gas emissions. Danish municipalities are interested in understanding the potentials for integrating various types of e-bikes in transportation planning. Increased use of such bicycles may contribute substantially to the greening of transportation and may also ease congestion and thus improve travel times. For indoor mobility, corridors and elevators represent bottlenecks for mobility in large building complexes (e.g. hospitals, factories and university campuses). With the addition of mobile robots, humans and robots will also be fighting to use the same space when moving indoors. Heavy use of corridors is also a source of noise that negatively impacts building occupants.

The ongoing, sweeping digitalisation has also reached outdoor and indoor mobility. Thus, increasingly massive volumes of mobility-related data, e.g. from sensors embedded in the road and building infrastructures, networked positioning (e.g. GPS or UWB) devices (e.g. smartphones and in-vehicle navigation devices) or indoor mobile robots, are becoming available. This enables an increasingly wide range of analyses related to mobility. When combined with digital representations of road networks and building interiors, this data holds the potential for enabling a more fine-grained understanding of mobility and for enabling more efficient, predictable, and environmentally friendly mobility. Long movement times equate with congestion and bad overall experiences.

The above data foundation offers a basis for understanding how well a road network or building performs across different days and across the duration of a day, and it offers the potential for decreased movement times by means of improved mobility flows and routing. However, there is an unmet need for low-cost tools that can be used by municipalities and building providers (e.g. mobile robot manufactures) that are capable of enabling a wide range of analytics on top of mobility data.

  1. Build extract-transform-load (ETL) prototypes that are able to ingest high and low frequency spatial data (e.g. GPS and indoor positioning data). These prototypes must enable map-matching of spatial data to open road network and building representations and must enable privacy protection.
  2. Design effective data warehouse schemas that can be populated with ingested spatial data.
  3. Build mobility analytics warehouse systems that are able to support a broad range of analyses in interactive time.
  4. Build software systems that enable users to formulate analyses and visualise results in maps-based interfaces for both indoor and outdoor use. This includes infrastructure for the mapping of user input into database queries and the maps-based display of results returned by the data warehouse system.
  5. Develop a range of advanced analyses that address user needs. Possible analyses include congestion maps, isochrones, aggregate travel-path analyses, origin-destination travel time matrices, and what-if analyses where the effects of reconstruction are estimated (e.g. adding an additional lane to a stretch of road or changing corridors). For outdoors settings, CO2-emissions analyses based on vehicular environmental impact models and GPS data are also considered.
  6. Develop transfer learning techniques that make it possible to leverage spatial data from dense spatio-temporal “regions” for enabling analyses in sparse spatio-temporal regions.
The envisioned prototype software infrastructure characterised above aims to be able to replace commercial road network maps with the crowd sourced OpenStreetMap (OSM) map and for indoors enable new data sources about the indoor geography. The open data might not be curated, which means that new quality control tools are required to ensure that computed travel times are correct. This will reduce cost. Next, the project will provide means of leveraging available spatial data as efficiently and effectively as possible. In particular, while more and more data becomes available, the available data will remain sparse in relation to important analyses. This is due to the cost of data that can be purchased and due to the lack of desired data. Thus, it is important to be able to exploit available data as well as possible. We will examine how to transfer data from locations and times with ample data to locations and times with insufficient data. For example, we will study transfer learning techniques for this purpose; and as part of this, we will study feature learning. This will reduce cost and will enable new analyses that where not possible previously due to a lack of data. Rambøll will be able to in-source the software infrastructure and host analytics for municipalities. Mobile Industrial Robotics (MiR) will be able to in-source the software infrastructure and host analytics for building owners. Additional value will be created because the above studies will be conducted for multiple transportation modes, with a focus on cars and different kinds of e-bikes. We have access to a unique data foundation that will enable these studies.

The project involves the research workstreams of Data Management (WS3), AI (WS2), and CyPhys (WS6), and Ethics (WS10) of DIREC.

May 1, 2021 – April 30, 2024 – 3 years

Total budget DKK 9,41 million / DIREC investment DKK 5,19 million

Participants

Project Manager

Christian S. Jensen

Professor

Aalborg University
Department of Computer Science

E: csj@cs.aau.dk

Ira Assent

Professor

Aarhus University
Department of Computer Science

Kristian Torp

Professor

Aalborg University
Department of Computer Science

Bin Yang

Professor

Aalborg University
Department of Computer Science

Martin Møller

Chief Innovation Officer

The Alexandra Institute

Mikkel Baun Kjærgaard

Professor

University of Southern Denmark
The Maersk Mc-Kinney Moller Institute

Norbert Krüger

Professor

University of Southern Denmark
The Maersk Mc-Kinney Moller Institute

Avgi Kollakidou

PHD FELLOW

University of Southern Denmark
The Maersk Mc-Kinney Moller Institute

Kasper Fromm Pedersen

Research Assistant

Aalborg University
Dept. of Computer Science

Partners

Categories
Bridge project

Deep Learning and Automation of Image-Based Quality of Seeds and Grains

Project type: Bridge Project

Deep Learning and Automation of Image-Based Quality of Seeds and Grains

Today, the manual visual inspection of grain is still one of the most important quality assurance procedures throughout the value chain of bringing cereals from the field to the table. In order to improve performance, robustness and consistency of this inspection, there is a need for automated imaging-based solutions to replace subjective manual inspection. In order to meet this need FOSS has developed a multispectral imaging system called EyeFoss™. With this system user independent multispectral images of +10.000 individual kernels can easily be collected within minutes real time on site. The EyeFoss™ applications currently cover wheat and barley grading.

To derive maximum value from the data there is a need to develop methods of training data algorithms to automatically be able to provide industry with the best possible feedback on the quality of incoming materials. The purpose is to develop a framework which replaces the current feature-based models with deep learning methods. By using these methods, the potential is significantly to reduce the labor needed to expand the application of EyeFoss™ into new applications; e.g. maize, coffee, while at the same time increase the performance of the algorithms in accurately and reliably describing the quality of cereals.

This project aims at developing and validating, with industrial partners, a method of using deep learning neural networks to monitor quality of seeds and grains using multispectral image data. The method has the potential of providing the grain industry with a disruptive new tool for ensuring quality and optimising the value of agricultural commodities. The ambition of the project is to end up with an operationally implemented deep learning framework for deploying EyeFoss™ to new applications in the industry. In order to the achieve this, the project will team up with DTU Compute as a strong competence centre on deep learning as well as a major player within the European grain industry (to be selected).

The research aim of the project is the development of AI methods and tools that enable industry to develop new solutions for automated image-based quality assessment. End-to-end learning of features and representations for object classification by deep neural networks can lead to significant performance improvements. Several recent mechanisms have been developed for further improving performance and reducing the need for manual annotation work (labelling) including semi-supervised learning strategies and data augmentation. Semi-supervised learning  combines generative models that are trained without labels (unsupervised learning), application of pre-trained networks (transfer learning) with supervised learning on small sets of labelled data. Data augmentation employs both knowledge based transformations, such as translations and rotations and more general learned transformations like parameterised “warps” to increase variability in the training data and increase robustness to natural variation.
The scientific value of the project will be new methods and open source tools and associated knowledge of their performance and properties in an industrial setup. For capacity building the value of the project is to educate one PhD student in close collaboration with FOSS – the aim is that the student will be present at FOSS at least 40% of the time to secure a close integration and knowledge exchange with the development team at FOSS working on introducing EyeFossTM to the market. Specific focus will be on exchange at the faculty level as well; the aim is to have faculty from DTU Compute present at FOSS and vice-versa for senior FOSS specialists that supervise the PhD student. This will secure better networking, anchoring and capacity building also at the senior level. The PhD project will additionally be supported by a master-level program already established between the universities and FOSS. Specifically, the project aims to provide FOSS with new tools to assist in scaling the market potential of the EyeFossTM from its current potential of 20m EUR/year. Adding, in a cost-efficient way, applications for visual inspection of products like maize, rice or coffee has the potential to at least double the market potential. In addition, the contributions will be of generic relevance to companies developing image-based solutions for food quality/integrity assessment and will provide excellent application and AI integration knowledge of commercial solutions already on-the-market to other Danish companies.

The project involves the research themes of AI (WS2) and CyPhys (WS6) of DIREC.

October 1, 2020 – September 31, 2024 – 3.5 years

Total budget DKK 3,91 million / DIREC investment DKK 1,90 million

Participants

Project Manager

Lars Kai Hansen

Professor

Technical University of Denmark
DTU Compute

E: lkai@dtu.dk

Kim Steenstrup Pedersen

Associate Professor

University of Copenhagen
Department of Computer Science

Lenka Hýlová

PHD Fellow

Technical University of Denmark
DTU Compute

Partners

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Bridge project

Edge-based AI Systems for Predictive Maintenance

Project type: Bridge Project

Edge-based AI Systems for Predictive Maintenance

Downtime of equipment is costly and a source of safety, security and legal issues. Today, organisations adopt a conservative schedule of preventive maintenance independent of the condition of equipment. This results in unnecessary service costs and occasional interruptions of production due to unexpected failures. Therefore, it is imperative in many domains to transition from regular maintenance to predictive maintenance. A recent report An AI nation: Harnessing the opportunity of artificial intelligence in Denmark estimates that enabling predictive maintenance via AI has a 14-19 billion potential for the Danish private sector.

Relevant domains include medical production (e.g. Novo Nordisk) to introduce condition-based maintenance of the machines that are used in production. The data collected by the equipment manufacturers is often not available in real-time. To address this issue, they need accurate predictive models, based on data collected by sensors under their control. Robot manufactures (e.g. UR) and their integrators want to enable condition-based maintenance of robotic systems. To address this, they need predictive models based on data from robots. In both domains due to reliability and safety requirements it is a prerequisite that data collection and processing are placed in vicinity of the equipment.

The energy sector (e.g. Energinet) wants to incorporate predictive knowledge of equipment performance. They need accurate predictive models, based on available data. E.g. for wind turbines based on local wind conditions as well as the state of the wind turbines. For this case Energinet has to collect the data externally as they do not have access to internal wind turbine data.

The research aim of the project is methods and tools that enable industry to develop new solutions with accurate AI-based maintenance predictions on edge-based software platforms.

The resulting applications must be deployed to collect and process large amounts of data locally. This data feeds high accuracy predictive models deployed at the edge, adapted to changing local conditions, and maintained with minimum intervention from operators.

These models should cover abstractions allowing the understanding of relevant dependencies in the data. The key research problem is to devise architectures and solutions that scale to the entire fleets of equipment with accurate AI predictions.

This also requires that resources in terms of processing power, storage and communication, are optimised in order to obtain low-power and real-time performance, leading to a resource efficiency vs prediction accuracy trade-off. The project will establish a bridge to enable Danish companies to develop and use AI-based predictive maintenance within several domains.

The scientific value of the project is new methods and tools and associated knowledge of their performance and properties in field tests. These are important contributions to provide excellent knowledge to Danish companies and education programs within AI and IoT.

For capacity building the value of the project is to educate 3 PhD students (including 1 Industrial PhD) and 1 Post Doc in close collaboration with industry. The open source availability of general project outcomes and industry collaboration enable several exploitation paths. In addition, for the master-level the projects will offer an industry program to 15 students at 3 universities.

The business and societal value is on a national level estimated to a 14-19 billion potential for the Danish private sector. In the project we target both the medical, robotic, industrial and energy sectors. These are Danish frontrunners in adopting the technology and creating inspiration for wider adoption by the Danish private sector. For the public sector enabling equipment with higher operation efficiency will positively impact the efficiency of the sector.

Within the area of AI the project will research and develop AI models applicable to predictive maintenance. Furthermore, research will consider methods for handling limitations on training data among others due to data ownership restrictions and confidentiality. A final aspect is work on AI models that can adapt to different edge conditions including available processing power and timing deadlines.

Within the area of Cyber-physical systems the project will research and develop software architectures and platforms for edge-based execution of AI models. The outcomes should enable AI models to adapt to changing local conditions, and be maintained with minimum intervention from operators. The key research problem is to devise architectures and solutions that scale to the entire fleets of equipment with accurate AI predictions. This requires methods that optimize resources in terms of processing power, storage and communication in order to obtain low-power and real-time performance, leading to a resource efficiency vs prediction accuracy trade-off.

October 1, 2020 – September 31, 2024 – 3.5 years

Total budget DKK 12.24 million / DIREC investment DKK 6.3 million

Participants

Project Manager

Mikkel Baun Kjærgaard

Professor

University of Southern Denmark
The Maersk Mc-Kinney Moller Institute

E: mbkj@mmmi.sdu.dk

Philippe Bonnet

Professor

IT University of Copenhagen
Department of Computer Science

Xenofon Fafoutis

Associate Professor

Technical University of Denmark
Dept. of Applied Mathematics and Computer Science

Jan Madsen

Professor

Technical University of Denmark
DTU Compute

Martin Møller

Chief Innovation Officer

The Alexandra Institute

Alexandre Alapetite

Software Solutions Architect

The Alexandra Institute

Niels Ørbæk Chemnitz

PhD fellow

IT University of Copenhagen
Department of Computer Science

Kasper Hjort Bertelsen

PhD Fellow

IT University of Copenhagen
Department of Computer Science

Emil Stubbe Kolvig-Raun

PhD Fellow

Universal Robots

Ahmad Rzgar Hamid

Software Engineer

University of Southern Denmark

Emil Njor

PhD Fellow

Technical University of Denmark

Partners

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Previous events

Digital Tech Summit 2021

Digital Tech Summit

November 30 – December 1, 2021
Øksnehallen, Copenhagen

Copenhagen Business School, Technical University of Denmark, University of Copenhagen, IT University of Copenhagen, University of Southern Denmark, Aarhus University, Roskilde University, Aalborg University, IT-Branchen and ­Teknologiens Mediehus invite you to Digital Tech Summit.

The largest academic based technology and ­business event in the Nordic countries

Digital Tech Summit brings together over 5,000 decision ­makers, engineers, companies, academia, startups, investors, and ­students, redefining tech leadership the digital sustainable tech transformation in industry and ­society.

What is Digital Tech Summit?

Digital technologies are driving change at all levels of society. Our vision is to create the leading research-based tech me-eting space in the Nordic Countries in order to interact and debate the most recent discoveries, participate in matchmaking events, and generate new ideas.

Digital Tech Summit is part conference, part exhibition and part networking activities where CEOs of technology companies, researchers, students, fast-growing startups, policymakers to ask a simple question: Where to next? Is it time to rethink our goals in a new world? And how can digital technologies like artificial intelligence, robots, cyber security, Internet of things, 5G take us further, create new jobs and solve some of the major societal challenges we face such as climate change, healthcare & disease prevention, etc.

Why Digital Tech Summit?

Why will thousands gather in Copenhagen? Four main reasons: valuable networking opportunities, incredible speakers, unique brand awareness and exposure at the greatest tech platform in Denmark and networking software – our conference App – that will maximise your returns, experience and learning.

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Events

DIREC Seminar 2022

DIREC Seminar 2022

26 – 27 SEPTEMBER 2022
HELNAN HOTEL MARSELIS – AARHUS

You are all invited to this year’s DIREC Seminar that will be full of workshops, talks, networking and more.

Please save the dates 26-27 September and join us at Helnan Marselis Hotel in Aarhus.

The full program will soon be available.

The seminar is relevant for all participants in DIREC workstreams and projects, as well as others who are professionally interested in participating in a DIREC workstream.

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Previous events

DIREC Seminar 2021

13 – 14 september 2021

DIREC Seminar 2021

The purpose is to give the participants in DIREC the opportunity to get to know each other, both professionally and personally / socially and thus create new networks across the universities and the Alexandra Institute.

We hope this will lead to the identification of new research issues, including grand challenges for digital technologies, collaboration across workstreams, and new projects with companies and the public sector.

Categories
News

Two new calls for DIREC Explore projects

7 September 2021

Call for project proposals: Young researchers and digital solutions for climate change

Explore projects are small agile research and innovation projects with the purpose to quickly screen new ideas within or between the core thematic topics of DIREC – possibly in relation to specific challenges of companies or society. Explore projects run for 3-12 months with a focus on identifying and creating new research challenges and areas.

Projects can be stand-alone projects or part of a sequential evolution of projects. For example, an Explore project may be a natural start to investigate into a new field or topic, which can lead to the creation of a larger research project.

DIREC is launching two special calls for Explore project proposals:

  • DIREC Climate Explore projects supports researchers wanting to explore how digital technology can help address some of the challenges related to climate changes. We are especially looking for ideas where digital technology has a centre role in the potential solution or where the research might make a significant impact
.
  • DIREC Starter Explore projects are targeted at researchers in the beginning of their carrier (up to 7 years after defending their PhD) and who have an idea for an excellent project within one or more of the workstreams of DIREC.

Each Explore project may be supported with up to DKK 300-500.000 including overhead for a period of up to a year.

We expect to start up to 10 explore projects during this round. 

Deadline for applying is November 5th, 2021.

Looking forward to seeing your proposals.

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News

Danish researchers will build a data warehouse to increase the possibilities with position data

7 September 2021

Danish researchers will build a data warehouse to increase the possibilities with position data

Incomplete data and different formats often make it difficult to integrate different position data and thus get the desired yield. A new collaboration between researchers at Aalborg University, Aarhus University and the University of Southern Denmark as well as Rambøll and the robot company MIR will make it easier to utilize the possibilities with position data.