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Nærings- og fiskerid

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Diverse

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Oslo

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LTP3 Styrket konkurransekraft og innovasjonsevne

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Verifisering, pilotering, demonstrasjon (ny fra 2014)

Controlling the flow of attention: Transition dynamics of attention and distraction across external and internal domains

We often choose to distract our minds, either externally (e.g., looking at our phone) or internally (e.g., thinking about weekend plans). It is currently not clear how, when, or why we (choose to) become distracted, nor do we have a mechanistic understanding of flow states, in which there is minimal distraction. This is a pressing issue, as distraction is omnipresent in our society, obstructing our capacity to focus both on personal goals and the many challenges humanity faces. To advance our understanding on how distraction occurs, we must overcome the current practice in which different attentional states are studied in isolation. I propose to capture the different research fields on attention in a single space defined by the dimensions “direction” (external vs. internal) and “locus” (on-task vs. distracted), distinguishing four attentional states (i.e., On-Task-External, On-Task-Internal, External Distraction, and Mind Wandering). The objective of the MIND-FLOW project is to map the dynamical transitions between these states and to causally influence the amount of time people spend in each of them. I will use behavioral measures, eye-tracking, and pupillometry to capture these states and quantify the dwelling times and a transition matrix through computational modeling. Inspired by recent empirical work on attention switching and neurobiological models of mind wandering, I hypothesize (i) the existence of an Off-Focus state through which these dynamical transitions occur, (ii) that internal bias and goal value determine which areas are visited most, and (iii) that contextual manipulations can alter the relative times spent in each area. I will test these predictions by a model-comparison approach and within-subject experimental manipulations of context volatility and task difficulty. Bridging these different fields of attentional research will increase cross-talk between them and provide building blocks for interventions targeting healthy attentional engagement.

Developing climate-resilient and eco-sustainable railway earthwork by mitigating geohazards while balancing biodiversity

The resilient and sustainable development of railway networks is fundamental for economic and social progress, especially under the current climate. To achieve this, we must embrace new innovative solutions while replicating best practices that benefit nature. Alongside increasing the infrastructure resilience and safety, we need transformative changes in railway earthwork asset development that go beyond prioritising decarbonisation and digitalisation and seek to restore nature, safeguard biodiversity and produce positive outcomes for human societies. Leveraging cutting-edge technologies like AI, earth observation, statistical analysis, advanced numerical modelling, laboratory tests and sensor technologies, RESOLVE seeks to integrate conventional engineering practices with life sciences, empowered by data science. Furthermore, RESOLVE will pioneer synergistic solutions that tackle climate change adaptation in railway earthwork assets while creating opportunities for nature restoration. This attempt involves developing nature-based solutions (NBS) and environmentally friendly construction materials, with the additional aim of fostering biodiversity restoration beyond ground reinforcement. Highly skilled researchers and practitioners, capable of dealing with such problems are scarce and in high demand by academia and industry. Thus, formed with 11 world leading research organisations and 3 companies across Europe and internationally with expertise and facilities in these areas, RESOLVE aims to ensure comprehensive, robust and implementable solutions are obtained for railway network climate resilience built and eco-sustainable development. The network is carefully designed to enables research and innovative staff exchange across all aspects. RESOLVE secondees will enjoy a highly integrated, interdisciplinary and intersectoral staff exchange, sharing know-how and skills development environment through the planned secondments, networkwide events and local trainings.

When a liquid droplet is placed on a solid surface with a temperature significantly higher than the boiling point of the liquid, the droplet hovers above the surface on a thin layer of its own vapor, a phenomenon known as the Leidenfrost effect. While this effect is highly undesirable in certain cooling applications due to the reduced energy transfer between the solid and evaporating liquid caused by the poor heat conductivity of the vapor, it can be of significant interest in various processes where avoiding contact with the surface is advantageous.  The LeidenForce project aims to comprehensively study the Leidenfrost effect and propose novel applications, either to mitigate its adverse effects or leverage its advantages. LeidenForce intends to (i) shift the fundamental understanding of the transition to the Leidenfrost state, (ii) optimize the heat transfer between the droplet and the substrate, (iii) utilize the isolated droplet to manipulate small amounts of liquid in unconventional scenarios (e.g., on a liquid surface, within a channel), and (iv) harness the vapor film to capture or confine particles using an external electrical field.  The practical implications will be leveraged by non-academic institutions involved in aviation (such as AIRBUS), metallurgy (CRM), cryogenics (Air Liquide), and space exploration (Centre Spatial de Liège). In aviation, managing cryogenic fuel is a crucial step toward achieving zero-emission flights. In metallurgy, innovative cooling methods will be developed based on mitigating the Leidenfrost effect. Air Liquide will address cooling issues by introducing particles into the evaporating liquid to modify the Leidenfrost effect. Lastly, at the Centre Spatial de Liège, Leidenfrost droplets will be utilised to delicately clean surfaces by trapping particles within these contactless droplets.

Advanced Leidenfrost Effect

Improvement of the quality and safety of fermented beverages through the management and control of spoilage microorganisms

SpoilControl will train the next generation of polyvalent researchers in the field of sustainable fermentation technologies, able to meet current and future challenges created by microbial spoilage and improve the quality & safety of fermented beverages. Europe is the historical leader of fermented drinks, whose competitiveness is intensively challenged. Microbial spoilage in fermented food is a growing concern from a financial viewpoint (economic losses) and from a health perspective (increasing presence of pathogens, particularly in improperly homemade or artisanal productions). Such quality & safety issues are amplified by societal and environmental changes: the consumer trend toward low-input products, sustainable practices, small-scale productions and climate changes are associated with increased risks of microbial spoilage. In addition, the fermentation sector cruelly lacks a global framework & training between beverages, resulting in efforts duplication & investments partitioning. SpoilControl will unfold an original strategy based on multiple beverages (wine, spirits, beer, cider, kombucha, kefir), multiple disciplines (from environmental & life sciences to engineering or economics), multiple solutions (development of sustainable biological, chemical and physical treatments). The consortium covers the whole fermentation chain from Fermentation to Glass with 34 partners including universities, SMEs, large companies, innovation cluster, start-up, analysis laboratories, technical institutes and homebrewers’ group. SpoilControl will have multiple impacts: at the scientific level, the study of non-conventional microbial species will be useful besides the fermentation sector; at the societal level, SpoilControl will improve public awareness of safety issues in fermented food and promote best practices for all end-users (industrials, consumers); the development of innovative solutions and treatments will have immediate and long-term economic impact.  

To develop autonomous robots that are able to comply with social conventions and expectations, and avoid rejection from humans requires that robots must be aware of the social context in which they operate. To this extent, robots need to be endowed with high levels of reactivity, proactivity, responsiveness, and intelligibility. The Doctoral Network - Industrial Doctorates on Social aWareness for sErvicE roboTs (SWEET) aims at training a new generation of research and professional figures able to advance the development of socially aware robots capable of perceiving, interpreting, and responding to human emotions, intentions, and cultural differences. The training program will offer a diverse curriculum, encompassing theoretical knowledge, hands-on technical skills, and real-world application scenarios. The network's interdisciplinary approach includes various fields, such as artificial intelligence, machine learning, human-robot interaction, computer vision, and cognitive sciences. Doctoral candidates will be immersed in cutting-edge research and innovation, gaining insights from the experience of both industrial and academic acclaimed research groups. The network will place a strong emphasis on ethical considerations and responsible innovation, deploying socially aware robots aligned with societal values and promoting inclusivity.  Doctoral Candidates will also have the opportunity to participate in a unique coaching program for continuous professional development of their soft and leadership individual skills. Integration Milestones following a Scenario-Based Learning approach will provide co-working activities where collaborative design/implementation is fostered. The inter-sectoral collaboration between academia, user groups’ representatives, business developers, and robot manufacturers of the project will further strengthen the novelty and impact of the research and training, and that research results are economically, socially, and technically feasible.

Social aWareness for sErvicE roboTs

Complex lipid membranes for science and technology

“CLIMB-Complex lipid membranes for science and technology” is a doctoral network of leading European lipid-research laboratories with highly complementary research expertise and training capabilities, which will allow participating PhD students to develop a wide range of research and transferable skills attractive to employers in both academic and industrial sectors. The research themes focus on the hypothesis that comprehensive understanding of the role of compositional complexity on “yet to be explored” structural properties and membrane-driven interactions is key to developing complex lipid-based nanomaterials that mimic the cellular environment and function as high-performance sustainable materials. A central aspect of the CLIMB programme is the development and physicochemical characterization of structure and stability of aggregates formed by novel lipid mixtures including natural extracts and novel protective polymer/peptoid-based coatings. Scientific results will be made available to non-academic partners to assess its possible translation into applications in the fields of mRNA delivery, bio-sensor development, and “green” formulations, areas in line with some of the grand challenges identified by EU. These objectives will be achieved by combining advanced and complementary experimental approaches from chemistry, physics, biology, and computational sciences. Through close collaboration with a network of both small and large industrial partners, the trainees will receive direct hands-on experience, and will gain insights into how lipid-based materials are used in innovative applications. In summary, CLIMB aims to advance the current knowledge on the interplay between structure, functions, interaction, and compositional complexity in lipid membrane. The generated results will contribute to important knowledge transfer between academia and the companies involved, promoting and increasing the success rate for innovations of grand societal challenges.

Random sampling for trustworthy pooling layers in graph neural networks

Graph neural networks (GNNs) are deep learning architectures that hold the promise of durably changing our way of, e.g., predicting inter-molecular chemical affinity to accelerate drug discovery or smartly balancing power loads on the electricity grid. My project focuses on pooling layers, a major building block of neural networks, including GNNs. In a nutshell, pooling layers generate local summaries of the data to enable faster computations and generate discriminative multiscale representations. The traditional periodic-sampling-based pooling that has proven so successful for data defined on regular grids such as time series or images becomes ill-defined when the underlying space of the data loses its regularity – such as data defined over graphs. Today’s existing solutions stem from a trial-and-error approach often driven by two practical and somewhat short-sighted objectives: computation efficiency and empirical predictive performance. The aim of my project is to add trustworthiness as a main objective in the design of novel pooling layers for GNNs. To achieve this, I will bridge the fields of signal processing, point processes, and random numerical linear algebra with graph machine learning to incorporate random methods (mainly random sampling and random projections) into the design of pooling layers for GNNs.  Indeed, randomized approaches are provably well adapted to three pillars of trustworthiness: technical robustness,  transparency, and privacy. The outcome of my project will be an extensively tested, theoretically-grounded,  trustworthy, ready-to-be-deployed, open-source, GNN-based toolbox, along with its user guide for practitioners. It will be the first GNN architecture benefiting from the theoretical guarantees random pooling layers come with. Such trustworthy GNNs will have an impact in the increasingly many fields in which GNNs are pushing the state-of-the-art: including circuit chip design,  physics, drug discovery and network neuroscience.

Breaching the boundaries of safety and intelligence in autonomous systems with risk-based rationality

Autonomous systems able to comprehensively assess and model risks would be a scientific breakthrough leading to highly intelligent system behaviors, safe interactions in unstructured environments, and public trust. Currently, risk perception is an important part of human cognition not adequately implemented in the situation awareness, control and decision-making of autonomous systems. Therefore, this project addresses fundamental research challenges related to risk-based autonomy and safe and efficient shared control with human supervisors. The aim is to develop powerful risk modelling and built-in risk control solutions, closely connected to risk-based decision support for human supervisors in-the-loop. The outcomes of the project will allow for new, safe, and advanced types of transport, mapping and monitoring of oceans and land, and inspections of structures difficult to access. Although there are a few preliminary concepts coupling risk models with control algorithms and artificial intelligence (AI), a fundamental framework providing risk-based control and decision support – for both the autonomous system and human supervisors – does not exist. In BREACH, I will establish such a framework constituted by quantitative risk models merged with data-driven approaches, simulation and model-based approaches, integrating hardware, software, human, and environmental risk elements. The complexity of the framework will gradually evolve in two parallel tracks along with demonstrations for mission planning and execution for autonomous marine “organizations”. The focus on marine systems is due to the demanding challenges in that domain, and my knowledge and experience as a researcher in the Centre of Excellence on Autonomous Marine Operations and Systems (NTNU AMOS). For several years, I have contributed to fundamental research related to risk management of autonomous marine systems, which has convinced me that this project will be a major leap forward with great impact.

NeuroNanotech will train eleven researchers to tackle one of the major challenges in Europe’s ageing population - neurological diseases. The ability to monitor and modulate neural activity using interfaces has enabled a better understanding of brain function and has led to therapeutic solutions for some neurological disorders. Yet, fundamental technological challenges, such as ensuring proper brain tissue interfacing and reliable long-term recording/stimulation after implantation, impede widespread clinical use. New approaches to provide effective treatments are urgently needed.  NeuroNanotech will develop novel nanostructured flexible neural interfaces with highly improved tissue integration, minimizing foreign-body reactions and tissue scarring and allowing stable stimulation treatments. As main innovation, we will develop minimally invasive, ultra-sensitive spintronic magnetic sensors able to record stably without interfering with stimulation signals and avoiding electrode degradation. Connecting both interfaces, we will construct a low-invasive, closed-loop neurostimulation system that integrates feedback signals from the neural activity and provides real-time stimulation of the target structures according to the patient´s needs.  To achieve these goals, NeuroNanotech brings together experts in nanotechnology, device engineering, neuroscience and clinical neurology. The individual research projects are highly interconnected, ensuring interdisciplinary training. Researchers will benefit from training in advanced research and relevant complementary skills, imparted by an international and intersectoral consortium of research institutes, universities, companies, hospitals and social organisations from 9 different countries. We will provide researchers a unique environment focused on innovation and collaboration, with a view to commercial applications of the research results. This framework will open researchers avenues in both academia and health-related industry.

Nanoplatforms for Neuroscience

Harnessing AI and Data-Intensive Technologies

The objective of HARNESS is to train a multidisciplinary cohort of 13 PhD students in an international Doctoral Network, specialising in ethics, law, and technology. The network will offer comprehensive training, supervision, and secondments, allowing individuals to become experts in their specific disciplines while gaining practical knowledge across various fields. The program is supported by an excellent team of partners who are world leaders in their respective domains.  The ethical and socio-technical impacts of the current and forthcoming AI and data-intensive information systems (e.g. Common European Data Spaces) require careful multidisciplinary consideration and foresight techniques regarding their alignment with EU's values, sovereignty and economic and societal goals. The new European regulations on data and AI should be supported by robust methods and tools to empower citizens and civil society to swiftly comprehend, navigate, and address emerging threats to fundamental rights. HARNESS will analyse the joint impact of new AI and data-intensive technologies and their regulation, considering legal entanglements, new legal compliance tools and the consequences that can be anticipated, from ethical, societal and economic perspectives. AI and data legislation will be formalised and operationalised under the 'Law as Code' paradigm using international standards and semantic web technologies. Specific cases such as AI Foundational models, impact of new regulations on competitiveness and the interplay of self-sovereign identity in Common European Data Spaces will be analysed in detail. HARNESS will utilise and build upon the state of the art in open information systems and knowledge engineering to develop appropriate methodologies, tools, and taxonomies to assist with regulatory compliance, deliberation and agreement on ethical norms. This will involve charting the constellation of possible systems, anticipating new risks and developing effective mitigations.

Midlene fra IKTPLUSS-IKT og digital innovasjon fordelt på år

Midlene fra IKTPLUSS-IKT og digital innovasjon fordelt på fylker

Midlene fra IKTPLUSS-IKT og digital innovasjon fordelt på fagområde

Senior Researcher

In-flight icing of UAVs is a meteorological risk that endangers UAVs and prevents safe operations in cold weather. UAV icing is an emerging research field where Norway is a key stakeholder – both because due to the cold climate UAV operations are often affected by icing, and also because Norway is leading on the topic in research (e.g. NTNU UAV Icing Lab, Met.no, UiT), industry (e.g. UBIQ Aerospace, Maritime Robotics, Avinor, Kjeller Vindtekknik), and defense (e.g. FFI). The 2nd International UAV Icing Workshop has positioned itself as the key networking and dissemination event for the topic of atmospheric icing on UAVs.The 2nd International UAV Icing Workshop is innovative in its focused approach on the emerging and crucial field of UAV icing. Distinctive for its specialized subject matter, the event bridges gaps between advanced technological fields like nanotechnology, advanced materials, and ICT with practical UAV applications. 

This event aligns with several key enabling technologies, which play critical roles in solving the icing challenge for UAVs. First and foremost, autonomy and control topics from the area of ICT are inherently linked with drone technologies. This relates for example to the development of smart algorithms for ice detection, digital twins for icing simulations and risk assessment, AI systems for autopilots and more. Furthermore, the field has a strong component related to nano-technology for the development of novel and innovative ice protection systems (e.g. usage of nano-carbon fiber paint for ice protection systems. Last but not least the area of icephobic materials – materials which passively reduce icing hazards by modifying the surface structure, similar to the Lotus-effect with water – is a field of advanced materials and nanotechnology. As such, this event and the stakeholders participating in it are going to contribute substantially to drive forward and generate cross-sectioral knowledge and innovation in these areas.

2nd International Workshop on Icing of Uncrewed Aerial Vehicles

Prosjektet omhandler utviklingen av et solid API og SDK til Muybridge's kjerneteknologi for å kunne levere sensorteknologi inn i flere vertikaler, og utnytte potensialet til det fulle.

Dette vil åpne opp for en OEM/lisensierings forretningsmodell i enkelte vertikaler som kan sammenlignes med selskaper som ARM, Dolby og Nordic Semiconductor.

API og SDK strekker seg i omfang fra generisk sensorkonfigurasjon til avanserte algoritmer for 3D rekonstruksjon, objekt-gjenkjenning og data-komprimering.

Weightless camera ADI/SDK

Prosjektet MultiView har tatt frem en kjerneteknologi som representerer nye sensor systemer og nettverk, som kan anvendes i en rekke industrier. I samarbeid med Simula Research Laboratory har Muybridge gjennomført FoU-aktiviteter innenfor kamera teknologier, høyhastighets transport og prosessering av bildedata. Forskningsresultatene vil levere løsninger inn i flere krevende industrier hvor visuell informasjon og sanntids bildeprosessering er svært viktig.

Prosjektet har tatt for seg forskning og utvikling innenfor følgende tre hovedområder:

I: Utvikling av algoritmer for behandling av bildedata som skal levere verdifull data til sluttbrukere, integrerte prosesser og autonome systemer.

II: Optimalisering av maskinvare arkitekturer for sanntids sensor systemer for å levere tidskritisk synkronisert bildedata med hastigheter inntil flere gigabit, med fokus på kost optimalisering, form faktor og et vesentlig redusert miljømessig fotavtrykk sammenlignet med eksisterende sensor systemer.

III: Optimalisering av prosessor arkitekturer for å levere prosessert bildedata i sanntid til integrerte prosesser, systemer og sluttbrukere for å ivareta dataens integritet.

The MultiView project will deliver a core technology, innovating sensor systems across several industries. In cooperation with Simula Research Laboratory, Muybridge will perform research on imaging technologies, high-speed data transport and data processing to develop solutions applicable in demanding industries, where visual information and reliant real-time image processing is crucial.

The project will be performing research in the three following areas:

I: Algorithms applied to imaging data in order to transform, combine and process sensor measurements of an arbitrary environment into valid and valuable data for both humans, preceding processes and autonomous systems.

II: Optimization of hardware architectures for real-time sensor systems in order to deliver reliable synchronized image data at several gigabit rates at an optimized hardware cost, small form-factor and with a heavily reduced environmental footprint compared to existing sensor systems.

III: Optimization of processing architectures to deliver processed image data in real-time to preceding processes, systems and users in order to maintain data validity and integrity.

0 projects

PES-HORISONT-EU-PES Horisont Europa

Weightless camera ADI/SDK

Awarded: NOK 74,999

Project Period: 2021-2021

Location: Akershus

BIA-Brukerstyrt innovasjonsarena

Multiple Camera Sensor System

Awarded: NOK 16.0 mill.

Project Period: 2021-2023

Location: Oslo