My name is Fasil Ayelegn Tassew, I joined DNV GL in February 2020. I work as a senior researcher in GTR maritime. My work focuses on zero-carbon fuels, decarbonization and synthetic fuels for maritime applications.
2020 has been a milestone year both personally and career-wise. The year could not have started better for me. On January 17, I successfully defended my PhD thesis and on the same day I got an offer for a position to join DNV GL. Within two weeks, I moved from Porsgrunn to Nesøya in Asker. I met my new colleagues and tried to adapt to open office as well as home office work environments as quickly as I can.
In my work, 2020 is also turning out to be a year where the cost of renewable electricity from Solar, wind etc. has finally become competitive with fossil electricity. This is an important milestone for a rapid adoption of green hydrogen with direct implications for synthetic fuels production, carbon-capture and overall decarbonization.
Despite the disruption caused by the pandemic, my first year in DNV GL has so far been exciting.
I joined the Oil & Gas program within DNV GL in August. This was an exciting opportunity, especially in such challenging times, to be part of a great environment focused on new technology to transform society and the industry.
Digitalization is one of the main transformations in the Oil & Gas industry, where we foresee that simulations will be increasingly important for real-time decision support providing new opportunities but also new failures and risks. My line of work lies in combining physics-based modelling with data-driven machine learning taking uncertainty into account. I’m thrilled to be part of a team that will develop methods and technologies to bridge the gap between physics-based and data-driven decision support, which can also be applied to different scientific areas.
I joined Precision Medicine, GTR in January 2020. Previously I worked in academic research, investigating digital therapeutics for cognitive decline apparent in certain neurological disorders. At DNV GL, my role is to understand how we can overcome some of the barriers hindering precision medicine today. One focus area has been understanding what encompasses trustworthy AI in the context of health, whilst another has been investigating federated networks as a potential solution to overcome the difficulties of data sharing. Data sharing is limited in healthcare due to a multitude of reasons, including the extremely sensitive nature of health data, privacy concerns and poor interoperability between healthcare institutes. Federated networks enable algorithms and queries to be shared and executed between partners, without patient data leaving its original location. This offers hope to precision medicine, as access to large amounts of high-quality data is vital for example to improve accuracy of diagnoses or tailor treatments for patients.
I joined the Digital Assurance Simulation Technology team in December 2020, after working with simulator technology and software development for Marine Cybernetics / Control Systems and Cybernetics Advisory for almost 10 years now.
My role for the past several years has been developing our application for dynamic capability studies, which we have shipped to customers around the world.
I will continue contributing on software development projects within GTR, primarily on the OSP platform and it’s pilots going into next year.
I joined the DNV GL Ocean Space Program in March 2020, days before the Norwegian COVID shutdown, with a background in marine technology, including a PhD about making special ships resilient to uncertain, rapidly changing mission requirements.
My current work aims to provide insights into the future of existing and emerging ocean industries, and I find it very rewarding to work on topics with potential strategic importance. In my current role, I have the opportunity both to advance as a systems thinker and to develop domain expertise within many interconnected aspects of the ocean economy, including socio-economic, technical, biological and environmental stressors.
My time with DNV GL so far is best characterized by learning, both from colleagues and with colleagues. I hope my contributions will lead to useful insights into the future of ocean industries, that can be used in the development of new business opportunities.
January 2020 kicked off an exciting year for me, and of course a very challenging one for the rest of the world. I joined the Energy Transition Team in GTR after six years in academic research on biofuel markets and their simulation. My colleagues on team made my start in DNV GL and in new country as easy as possible. They patiently brought me up to speed on the Energy Transition Outlook Model, and then fully involved me in the inspiring work of making DNV GL a brandmark as a knowledge house for the energy transition, providing insights to both DNV GL internally and external customers. My contribution to the Energy Transition Outlook this year has focused especially on transport, hydrogen and modelling aspects of manufacturing. Besides the work on the Energy Transition Outlook, I also contributed to the upcoming Ocean Space Forecast with conceptualization and modelling.
What impressed me most in my first year in DNV GL and in the Energy Transition Team, is the high commitment to the goals of DNV GL and the high level of taking care of each other. These helped to overcome the turmoil caused by the COVID-19 pandemic.
I am very fortunate to work within a great Team of dedicated, skillful and kind colleagues and it’s inspiring to see our outcomes being used to steer the Energy Transition to a more sustainable future.
The emerging disruptive technologies are changing our world in different ways. I am proud to know that DNV GL is not only embracing the new cutting-edge technologies to support our business but also working on managing risks that these technologies may bring in.
Moving from academic area to GTR is an exciting opportunity, which enables me to leverage the latest advances in research, especially the AI and IoTs technologies, to solve practical problems. Furthermore, it is interesting to work on cross-domain project and inspiring to collaborate with experts in an international way.
After a few months’ effort and hardworking, we released our first delivery in Phase I – Battery.ai 1.0, which takes advantage of deep learning, empirical models and domain knowledge support from energy section to enhance our battery degradation analysis. And I am happy to know that this project attracts extensive interest both from internal and external sides.