BRIDGITISE PhD candidates will benefit from:
• An interdisciplinary and collaborative environment where innovation thrives.
• Coordinated research supervision involving academic and industrial partners.
• Development of data analytical skills to manage complex topics.
• Training in effective communication and writing to engage both technical and non-technical audiences.
List of open PhD positions:
Overall eligibility criteria
Applicants must comply with MSCA eligibility criteria:
• At the date of the recruitment they must not possess a doctoral degree.
• They must comply with the mobility rule that is: they must not have resided or carried out their main activity (work, studies, etc.) in the country of the recruiting beneficiary for more than 12 months in the 36 months immediately before their recruitment date.
• They must comply with the profile described for the position.
Please find the additional information regarding the rights and obligations of MSCA fellows, regarding employment conditions, integrity, and excellence here.
Monthly salary
The Marie Skłodowska-Curie Actions (MSCA) programme offers a highly competitive and attractive salary and working conditions. The successful candidates will receive a gross salary in accordance with the MSCA regulations for doctoral candidates.
Exact gross salary will be confirmed upon appointment (employer costs and other deductions depend on recruiting host): living allowance = €3.400/month (correction factor to be applied per country) + monthly mobility allowance = €600. An additional monthly allowance of €660 is applicable depending on the family situation. The gross salary indicated above is paid for a maximum of 36 months.
For longer duration of the DC project, specific agreement with the host institution can be made.
Due to difference in the correction factor between the different countries the salary may change when the DCs change beneficiary (1 academic and 1 industrial).
In addition to their individual scientific projects, all fellows will benefit from further continuing education, which includes secondments (internships), a variety of training modules as well as transferable skills courses and active participation in workshops and conferences.
Within BRIDGITISE each recruited researcher will for most projects spend one secondment period at one of the other complementary Beneficiaries or Associated Partners.
How to apply:
Send your application to bridgitise@gmail.com
OPEN POSITION: DC4 – WIM – Advanced Bridge Weigh in Motion (B-WIM) performance
Host institutions: Slovenian National Building and Civil Engineering Institute (ZAG), Cestel (CES)
Secondment: University of Twente (TWE)
Country: Slovenia, Italy, Netherlands
PhD enrollment: University of Ljubljana
Duration: 36 months
Supervisors: A. Žnidarič, A. Anžlin (ZAG), M. Skobir (CES)
Objectives: Leverage vision-based monitoring and machine learning (ML) to enable enforcement-based BWIM
Expected Results: (1) Labelled database (2) ML classification tool (3) Validated prototype of ML-BWIM system (4) Accepted peer-reviewed papers. (5) Successfully defended PhD thesis.
Description: Prolonging the lifetime of highway bridges based on measured traffic loads is essential for reducing resource exploitation and traffic delays. Financially, this can result in millions of Euros annual savings in maintenance and user costs. Bridge weight in motion systems (B-WIM) can efficiently collect this data, however, their accuracy and reliability do not meet the metrology standard requirements, preventing their use in legal enforcement applications. In this project, a next-generation B-WIM system will be developed leveraging machine learning (ML) technology to process the vast amount of data from different sources (traditional B-WIM, traffic cameras etc.) collected by the industrial supervisor CES. Supported by the expert knowledge of the academic supervisor ZAG, the DC will form data pillars composed of B-WIM measured and processed strain data and synchronized photos acquired by traffic cameras. During the secondment at TWE, a vision-based algorithm will be built to extract vehicle type and dimensions from these photos. Under the supervision of POB, both data pillars will be used to develop a next-level ML classification tool, a prerequisite for a more accurate B-WIM system that will comply with metrology requirements. The ML tool will (i) identify critical aspects in the current B-WIM data
processing and (ii), through a sensitivity analysis, recognize data types with the most influential impact on the accuracy and reliability of B-WIM results. Finally, the enriched ML-BWIM will be validated in normal operating conditions under the supervision of CES.
Required profile/research interests:
• The candidate must hold a 2nd level Master of Science (120 ECTS + 180 ECTS in a bachelor degree) or equivalent degree in Engineering or in fields closely related to the project’s subject matter.
• Candidates who are not in possession of the required qualification at the call closing date may apply but
the academic qualifications must be awarded before enrollment.
• The final score obtained for the Master degree must not be lower than C+ in ECTS grading system
• The candidates must comply with the mobility rule: they must not have resided or carried out their main activity (work, studies, etc.) in the country of the first recruiting beneficiary (ZAG institute, Slovenia) for more than 12 months in the 36 months immediately before their recruitment date.
• Certified knowledge of English is required if not native speakers.
OPEN POSITION: DC8 – CODES – Small data becoming big data
Host institutions: RAMBOLL DANMARK (RAM); North Consulting (NOR)
Secondment: Politecnico di Milano (POLIMI)
Country: Denmark, Italy
PhD enrollment: Politecnico di Milano (POLIMI)
Duration: 36 months
Supervisors: Dr. Joan Hee. Roldsgaard (RAM); M.H. Faber (NOR)
Objectives: To develop a methodology to share and integrate monitoring information into bridge design codes
Description: The uncertainty affecting the prediction of the structural behavior and loading is normally represented by safety factors in the design codes. These factors do not account for the availability of information. The access to digital information for bridges has increasingly grown in the last decade. This includes both information gained from sensors, but also inspections, as-built information etc. Their availability has significantly enhanced the modelling of the actions (loading) and their effects for different bridge types. Recently, researchers have been focused upon quantifying the uncertainty of monitoring data to be used in the value quantification. However, bridge specific or bridge type-specific information is not used in a broader context or in a more generic well-defined context such as how much the potential reduction in safety factor can be based on monitoring data. Design and asset management of a larger group of bridges with similar properties can take advantage of the knowledge gained from the small pool of bridges, where a detailed understanding of the loading and the load effects has been established. This project will develop a framework to (a) codify and publicly share monitoring information collected from a limited pool of bridges of different types. This will include the adoption of robust and reliable online sampling from multiple sources and clients to improve the quality of the data; (b) the extraction of correlations in the monitoring data gathered from a limited pool of different bridges for the creation of a probabilistic prediction model of a bridge to be used in general; (c) the updating of the probabilistic model using monitored data; d) the identification of the process to use the probabilistic model to update design codes in adapted online versions.
Expected Results: (1) recommendations for reliable and robust online sharing of small data; (2) a validated general probabilistic prediction model of a bridge updated with monitoring information; (3) Accepted peer-reviewed papers. (4) Successfully defended PhD thesis.
Required profile/research interests:
• The candidate must hold a 2nd level Master of Science (120 ECTS + 180 ECTS in a bachelor degree) or equivalent degree in Engineering or in fields closely related to the project’s subject matter.
• Candidates who are not in possession of the required qualification at the call closing date may apply but
the academic qualifications must be awarded before enrollment.
• The final score obtained for the Master degree must not be lower than C+ in ECTS grading system
• The candidates must comply with the mobility rule: they must not have resided or carried out their main activity (work, studies, etc.) in the country of the first recruiting beneficiary (ZAG institute, Slovenia) for more than 12 months in the 36 months immediately before their recruitment date.
• Certified knowledge of English is required if not native speakers.
OPEN POSITION: DC13 – TWINS: Probabilistic Digital Twins for continuous bridge performance modelling
Host institutions: Sacertis Ingegneria (SAC); North Consulting (NOR)
Secondment: Politecnico di Milano (POLIMI)
Country: Denmark, Italy
PhD enrollment: Politecnico di Milano (POLIMI)
Duration: 36 months
Supervisors: G. Mancini (SAC); M.H. Faber (NOR)
Objectives: Develop a probabilistic digital twin model for optimal maintenance management of prestressed concrete bridges.
Expected Results: (1) PDT model of the bridge and damage classification model; (2) procedure to identify the optimal maintenance schedule; (3) Accepted peer-reviewed papers. (4) Successfully defended PhD thesis.
Description: The multiplicity of possible different and possibly interacting processes contributing or governing deterioration processes in bridges requires systems representations way beyond the deterministic approach where there is only focus on one or two processes and corresponding models. Currently, there is only limited knowledge on indicators and techniques effective to correlate the observable performance of pre-stressed structures with their structural reliability. A Probabilistic Digital Twin (PDT), supported by SHM and semi-big data from observations and monitoring, can provide a novel contribution to solve this issue and may support integrity management with knowledge improved substantially over time. In this project, an approach to build the PDT of a bridge will be developed making use of nonlinear FE-modelling of the effect of the deterioration. SAC will make available the FE model of a pre-stressed bridge under continuous monitoring that exhibits typical deterioration processes. The DC will use the model to simulate the structural responses under deterioration progressing in time. These responses will be used to develop a PDT model of the bridge that will be utilized as a representation of the best available knowledge and applied to generate (Big Data) simulations of very significant numbers of structural performance scenarios over time; including degradation developments of different origins (environmental factors, loading history, etc.), observations (dynamic responses, displacements, etc.) together with noise associated with different monitoring and inspection devices. Based on the established Big Data, classification schemes will then be utilized to identify how observable structural performances relate to different types and levels of degradation. The PDT will be used during the secondment at POLIMI, as a base to model the structural performances of the bridge under given loading and environment scenarios and identify a procedure to develop the optimal maintenance schedule, based on reliability requirements.
Required profile/research interests:
• The candidate must hold a 2nd level Master of Science (120 ECTS + 180 ECTS in a bachelor degree) or equivalent degree in Engineering or in fields closely related to the project’s subject matter.
• Candidates who are not in possession of the required qualification at the call closing date may apply but
the academic qualifications must be awarded before enrollment.
• The final score obtained for the Master degree must not be lower than C+ in ECTS grading system
• The candidates must comply with the mobility rule: they must not have resided or carried out their main activity (work, studies, etc.) in the country of the first recruiting beneficiary (ZAG institute, Slovenia) for more than 12 months in the 36 months immediately before their recruitment date.
• Certified knowledge of English is required if not native speakers.