Update index.md

jobs/PhD2024_NGF/index.md

@@ -1 +1,68 @@
 
+---
+layout: page
+title:  PhD position vacancy
+permalink: /jobs/PhD2024_NGF/
+---
+
+
+
+<div class="row">
+	<div class="col-sm-4 hidden-xs nopadding"><img class="img-responsive" src="{{ "img/3.png" | prepend: site.baseurl }}"></div>
+  <div class="col-sm-4 hidden-xs nopadding"><img class="img-responsive" src="{{ "img/4.png" | prepend: site.baseurl }}"></div>
+  <div class="col-sm-4 hidden-xs nopadding"><img class="img-responsive" src="{{ "img/2.png" | prepend: site.baseurl }}"></div>
+</div>
+
+- - - 
+
+
+## PhD Position in Data- and AI-driven Transportation Infrastructure Life-Cycle Extension 
+
+The 3D Geoinformation Group and AiDAPT Lab invite applications for a funded PhD position in the area of Data-/AI-driven infrastructure inspection and maintenance planning. We are looking for candidates highly motivated to work at the confluence of AI, geoinformation, infrastructure safety & sustainability, and algorithmic decision-making, in order to address the life-cycle extension needs of our aging and growing transportation systems. 
+
+A significant part of our transportation infrastructure, including roads and bridges, has reached or exceeded its design life. To extend the life of infrastructure in the future and at the same time meet sustainability goals, it is necessary to optimize interventions by predicting the most effective inspection and maintenance strategies, while reducing emissions, and network disruptions due to construction works or poor asset structural conditions, among others.
+
+This PhD research will investigate how optimal inspection and maintenance strategies can be devised using geo-data, engineering models, and AI. The case study will be the city of Amsterdam, however, the methods to be developed will be generic. The PhD research will commence with an inventory of the already existing inspection and maintenance data and will first develop a method to get structured insight into degradation over time based on these historical geo-based data. In a second step, the method will be further developed to propose optimal inspection and maintenance strategies by incorporaing the various factors affecting the condition of infrastructure and its stochastic degradation over time. The method will be validated using historical evolution of road and bridge asset conditions. It will result in a probabilistic digital twin based on which we can simulate intervention actions as the third step, such as repairs, upgrades, inspections, and monitoring system installations. The effects of such interventions will be modeled as an advancement of the digital twin in a fourth step, including improvements to asset condition, delays in deterioration processes, and uncertainty in damage detection. The direction that we intend to explore is developing a novel AI pipeline for this purpose to model the effects as key performance indicators. AI models will interact with the digital twin to generate and optimize policies on when and where to inspect and perform maintenance, based on expected degradation and meeting multiple sustainability goals. For the AI pipeline, the potential for obtaining training data to establish key performance indicators from simulations will be explored, such as traffic changes, carbon emissions, and climate change risks.
+
+The candidate's background can further motivate directions of this PhD project in coordination with supervisors and stakeholders.
+
+The PhD candidate will combine expertise on geo-data, digital twins, and algorithmic decision-making. She/he/they will be supervised by Jantien Stoter (3D Geoinformation Group) and Charalampos Andriotis (AiDAPT Lab). The candidate will work as part of a team within the groups, closely collaborating with researchers, engineers, and stakeholders associated with the City of Amsterdam and the Amsterdam Institute of Advanced Metropolitan Solutions. The research will be carried out in the context of the National Growth Fund project “Future proof living environment”. The position is funded for a duration of 4 years, during which the candidate will undertake research on their PhD topic and  contribute to the research agenda of the research groups, also dependent on the interest of the candidate.
+
+
+## Research groups
+
+The [3D geoinformation research group](https://3d.bk.tudelft.nl) focuses on the technologies underpinning geographical information systems (GIS), and aims at designing, developing, and implementing better systems to model 3D cities, buildings and landscapes. It is a multidisciplinary group of around [25 people](https://3d.bk.tudelft.nl/about/), including computer scientists, geomatics engineers, and geographers; 6 of them are tenured staff (1 professor, 2 associate-professor, and 3 assistant-professor). It has a history of successful collaborations with the industry and the government: its research has led to [open source software](https://github.com/tudelft3d) and standards for the management and use of 3D geo-data. 
+
+[AiDAPT](https://www.tudelft.nl/ai/aidapt) is TU Delft’s AI-Lab for Design, Analysis and Optimization in the Faculty of Architecture & Built Environment. The lab conducts research on decision-making under uncertainty for structural and architectural systems, at the intersection of risk & reliability, optimization, and machine learning. It is a vibrant team of engineers and computer scientists that work on novel physics-based and data-driven frameworks to understand, control, and improve built environment resilience and sustainability. As part of the [TU Delft AI Initiative](https://www.tudelft.nl/ai/tu-delft-ai-initiative), AiDAPT has links with other Faculties and the [ELLIS](https://www.tudelft.nl/ellis-delft-unit) Unit.
+
+## Doing a PhD at TU Delft
+
+At the Delft University of Technology, a PhD student is a full-time employee of the university who gets paid a salary, no extra funding is necessary.
+The gross salary is €2,125 per month for the 1st year, going up to €2,717 during the 4th year.
+TU Delft also offers an attractive benefits package, including a flexible work week and the option of assembling a customised compensation and benefits package.
+More information about doing a PhD at TU Delft and in the Netherlands can be found [here](http://www.phd.tudelft.nl) and [there](http://www.studyinholland.nl/education-system/degrees/phd).
+
+
+## Job requirements
+
+•	MSc degree (or almost completed) in computer science, geoinformatics/geomatics, engineering, or related discipline.
+•	Background in at least two of the following domains: structure & infrastructure modelling, algorithmic decision-making, transportation engineering, uncertainty quantification, geo-data analytics, machine learning, and optimization.
+•	Experience with data-driven and/or deep learning approaches and/or algorithmic decision-making,
+•	Interest in applying AI in infrastructure networks;
+•	Good programming skills in Python;
+•	Familiarity with deep learning frameworks such as Pytorch and Tensorflow (preferred); 
+•	Excellent oral and written communication skills in English proven by a minimum score of 100 in TOEFL or IELTS of 7.0 per sub-skill (writing, reading, listening, speaking). For more details please check the [Graduate Schools Admission Requirements](https://www.tudelft.nl/onderwijs/opleidingen/phd/admission). 
+•	Ability to work in a team, take initiatives, and be results-oriented.
+
+## How to apply
+
+•	Detailed CV - highlight examples of projects and/or achievements that demonstrate skills relevant to the advertised position;
+•	Motivation letter (no more than 600 words) addressing your interests and describing how your experience and plans fit with the position;
+•	Contact information for two references (letters not required at this stage);
+•	MSc thesis (if finished) and, if applicable, one or two notable publications you have co-authored;
+•	Undergraduate and graduate transcripts  (if finished).
+
+The position will remain open until May 29th, 2024. Applicants are encouraged to apply as soon as possible, because screening of applications and interviews with candidates may begin before the deadline. The starting date is 15th of August or 1st of September 2024.
+
+For more information about this position, please contact directly [Prof. Dr Jantien Stoter](http://3dgeoinfo.bk.tudelft.nl/jstoter) (<[email protected]>) or [Dr Charalampos Andriotis](https://www.cpandriotis.com) (<c.andriotis.tudelft.nl>).
+