ILO Jobs Internship Programme
https://jobs.ilo.org/content/Internships/?locale=en_GB
https://jobs.ilo.org/content/Internships/?locale=en_GB
To apply for an internship at the ILO, you must be enrolled in the last year of a graduate degree (Master or equivalent) or in a higher degree programme relevant to the ILO's work or have completed such a programme in the year preceding the application. You must also possess a working knowledge of at least one of the ILO's working languages (English, French or Spanish) and be able to adapt to an international, multicultural, multilingual environment. There is no age limit for applicants.
https://jobs.ilo.org/content/Internships/?locale=en_GB
https://jobs.ilo.org/content/Internships/?locale=en_GB
ኢትዮኮን ሰሞኑን በኤግዚቢሽን ማዕከል ትልቅ የኮንስትራክሽን ኤግዚቢሽን ዝግጅት አዘጋጅቶ በዚህም የኮንስትራክሽን ቤተሰቦች ብዙ የልምድ ልውውጥ ያደረጉበት እድል የተፈጠረበት ዝግጅት እንደነበር ለማወቅ ችለናል።
እዚህ ላይ የተያያዘውም መጽሔት በኢትዮኮን ለሕትመት በቅቶ ለአንባብያን ተደራሽ ሲሆን፣ ለዚህ ጽሑፍም የኢትዮጵያ ቢም ሶሳይቲ የፅሑፍ አስተዋፅዖ አድርገናል በገፅ 38 እና 40 ላይ ይመልከቱ እያልን በአጠቃላይም መፅሔቱ ሌሎችም ጥሩ መረጃዎችን የያዘ በመሆኑ እንድታነቡት ተጋብዛችኋል።
እዚህ ላይ የተያያዘውም መጽሔት በኢትዮኮን ለሕትመት በቅቶ ለአንባብያን ተደራሽ ሲሆን፣ ለዚህ ጽሑፍም የኢትዮጵያ ቢም ሶሳይቲ የፅሑፍ አስተዋፅዖ አድርገናል በገፅ 38 እና 40 ላይ ይመልከቱ እያልን በአጠቃላይም መፅሔቱ ሌሎችም ጥሩ መረጃዎችን የያዘ በመሆኑ እንድታነቡት ተጋብዛችኋል።
Project Information Requirements are the bedrock of a successful project –
and the more you make of them, the better-managed your project will be,
suggests Iain Miskimmin.
Project Information Requirements (PIR) can be hit and miss. On many of the projects
I have advised and assisted over the years, they are happily forgotten or merged into
the asset or engineering worlds. Their importance, and specifically their importance
to the people who finance and own the project, is not communicated. Yet this is
where we can win a significant battle to get investment in digital delivery....Read more
and the more you make of them, the better-managed your project will be,
suggests Iain Miskimmin.
Project Information Requirements (PIR) can be hit and miss. On many of the projects
I have advised and assisted over the years, they are happily forgotten or merged into
the asset or engineering worlds. Their importance, and specifically their importance
to the people who finance and own the project, is not communicated. Yet this is
where we can win a significant battle to get investment in digital delivery....Read more
Project Information Requirements.pdf
498.5 KB
One app for all your Word, Excel, PowerPoint and PDF needs. Get the Microsoft 365 app: https://aka.ms/GetM365
Dear valued members of our channel,
We have recently discovered that a significant number of Telegram accounts in Ethiopia have been hacked/compromised, resulting in the unauthorized dissemination of messages containing inappropriate content.
We kindly urge you to exercise caution and refrain from opening any messages that lack descriptions and appear to be designed to attract you to open them. It has come to our attention that a few such messages were sent to our channel by some of our members, and we promptly deleted them.
Thank you for your understanding and cooperation in maintaining a safe and respectful environment within our channel.
We have recently discovered that a significant number of Telegram accounts in Ethiopia have been hacked/compromised, resulting in the unauthorized dissemination of messages containing inappropriate content.
We kindly urge you to exercise caution and refrain from opening any messages that lack descriptions and appear to be designed to attract you to open them. It has come to our attention that a few such messages were sent to our channel by some of our members, and we promptly deleted them.
Thank you for your understanding and cooperation in maintaining a safe and respectful environment within our channel.
Join us on the "BIM Heroes" community discussion platform.
Be part of a movement to provide better building and infrastructure support for all humanity, through "Better Information Management", positively impacting productivity, outcome, safety and sustainability in the planning, design, construction, and management of the built environment.
It's a place to connect, engage, network, share, teach, learn and grow with other AEC professionals interested in 'digital transition' and adoption of BIM.
Sileshi Tesema
https://www.bimhero.io/share/gYwVVwCjJ2jGegEu?utm_source=manual
Be part of a movement to provide better building and infrastructure support for all humanity, through "Better Information Management", positively impacting productivity, outcome, safety and sustainability in the planning, design, construction, and management of the built environment.
It's a place to connect, engage, network, share, teach, learn and grow with other AEC professionals interested in 'digital transition' and adoption of BIM.
Sileshi Tesema
https://www.bimhero.io/share/gYwVVwCjJ2jGegEu?utm_source=manual
Phase 5 Africa Catalyst awards are available for professional engineering bodies in sub-Saharan Africa for one-year projects. The aim of this round of funding is two-fold: to support new ‘pilot’ initiatives and to communicate, disseminate and exploit impact from successful projects from the Pilot, Phase 2, Phase 3 and Phase 4 cohorts.
Learn more about the programme expectations and the application process through the applicant guidance notes. The deadline for applications is 28 November, 1:00pm GMT.
How to apply
https://raeng.org.uk/programmes-and-prizes/programmes/international-programmes/africa-catalyst/how-to-apply
Learn more about the programme expectations and the application process through the applicant guidance notes. The deadline for applications is 28 November, 1:00pm GMT.
How to apply
https://raeng.org.uk/programmes-and-prizes/programmes/international-programmes/africa-catalyst/how-to-apply
EBIMS BIM & Digital Transformation
Phase 5 Africa Catalyst awards are available for professional engineering bodies in sub-Saharan Africa for one-year projects. The aim of this round of funding is two-fold: to support new ‘pilot’ initiatives and to communicate, disseminate and exploit impact…
A good question has been asked by one of our follower on the comment page.
The question says, Is Ethiopia included by the programme?
Unfortunately Ethiopia is not included in the list, it is bizarre not to see Ethiopia in the list as Eritrea, Djibouti, Kenya are all in there.
Here is a note from the Guidance document:
The following countries are included in the program. These are:
Angola; Benin; Burkina Faso; Burundi; Cape Verde; Central African Republic; Chad; Comoros; Democratic Republic of the Congo; Djibouti; Eritrea; The Gambia; Guinea; Guinea Bissau; Kenya; Liberia; Lesotho; Madagascar; Malawi; Mali; Mauritania; Mauritius; Mozambique; Niger; Réunion; Rwanda; Sao Tome and Principe; Senegal; Seychelles; Sierra Leone; Somalia; South Africa; South Sudan; Sudan; Tanzania; Togo; Uganda; Western Sahara and Zambia.
On the final note it says, the following statement.
Institutions from ineligible countries within sub-Saharan Africa can still participate in projects as partners.
The question says, Is Ethiopia included by the programme?
Unfortunately Ethiopia is not included in the list, it is bizarre not to see Ethiopia in the list as Eritrea, Djibouti, Kenya are all in there.
Here is a note from the Guidance document:
The following countries are included in the program. These are:
Angola; Benin; Burkina Faso; Burundi; Cape Verde; Central African Republic; Chad; Comoros; Democratic Republic of the Congo; Djibouti; Eritrea; The Gambia; Guinea; Guinea Bissau; Kenya; Liberia; Lesotho; Madagascar; Malawi; Mali; Mauritania; Mauritius; Mozambique; Niger; Réunion; Rwanda; Sao Tome and Principe; Senegal; Seychelles; Sierra Leone; Somalia; South Africa; South Sudan; Sudan; Tanzania; Togo; Uganda; Western Sahara and Zambia.
On the final note it says, the following statement.
Institutions from ineligible countries within sub-Saharan Africa can still participate in projects as partners.
Africa Intelligence Hackathon Competition from Abrhot Library, Ethiopia. Watch the full video.
https://fb.watch/ojiLMywpWd/
https://fb.watch/ojiLMywpWd/
BIM, Data Analytics and Machine Learning:
The integration of Building Information Modeling (BIM) with data analytics and machine learning brings about a powerful combination that enhances the capabilities and insights derived from construction and building management. Here are some key aspects of how BIM, data analytics, and machine learning intersect:
1. Predictive Analytics for Design Optimization:
Machine learning algorithms can analyze historical project data within the BIM environment to identify patterns and trends. This information can be used for predictive analytics, helping architects and designers optimize future designs based on the performance and outcomes of similar projects.
2. Cost Estimation and Resource Planning:
Data analytics and machine learning can assist in more accurate cost estimation by analyzing historical cost data and project parameters within BIM. This helps in better resource planning and budgeting, minimizing the risk of cost overruns.
3. Performance Analysis and Optimization:
Machine learning algorithms can analyze real-time data from sensors embedded in smart buildings, which is integrated with BIM. This allows for continuous performance monitoring, and insights can be used to optimize energy efficiency, space utilization, and overall building performance.
4. Risk Management:
Data analytics can be employed to assess and predict project risks by analyzing historical data on similar projects. Machine learning algorithms can identify potential risk factors and recommend strategies to mitigate risks, improving overall project management.
5. Supply Chain Optimization:
Machine learning can be applied to optimize the supply chain by analyzing historical data on material usage, delivery times, and costs. This information, when integrated with BIM, allows for better decision-making in sourcing and logistics, improving efficiency and reducing delays.
6. Construction Monitoring and Quality Control:
Machine learning models can analyze construction site data, such as images from drones or surveillance cameras, to monitor progress and quality. Deviations from the planned construction process can be identified early, allowing for timely interventions to maintain quality standards.
7. Facility Management and Maintenance:
Data analytics and machine learning can assist in predicting equipment failures or maintenance needs based on historical performance data from BIM. This proactive approach to facility management helps in minimizing downtime and optimizing maintenance schedules.
8. Occupant Behavior Analysis:
Machine learning algorithms can analyze data on occupant behavior within buildings, such as temperature preferences, space utilization, and traffic patterns. This information, when integrated with BIM, helps in designing and managing spaces that cater to the needs and preferences of occupants.
9. Life Cycle Assessment:
Data analytics and machine learning can contribute to life cycle assessments by analyzing environmental impact data associated with building materials and construction processes. This information aids in designing sustainable buildings and making informed decisions about material choices.
10. Interoperability and Standardization:
Data analytics can be used to streamline interoperability between various BIM software tools by establishing and adhering to data standards. Machine learning can assist in automating data integration processes, making it easier for stakeholders to work with diverse datasets.
The integration of BIM, data analytics, and machine learning holds significant potential to transform the construction and building management industry, providing more efficient, cost-effective, and sustainable solutions across the entire lifecycle of a project.
The integration of Building Information Modeling (BIM) with data analytics and machine learning brings about a powerful combination that enhances the capabilities and insights derived from construction and building management. Here are some key aspects of how BIM, data analytics, and machine learning intersect:
1. Predictive Analytics for Design Optimization:
Machine learning algorithms can analyze historical project data within the BIM environment to identify patterns and trends. This information can be used for predictive analytics, helping architects and designers optimize future designs based on the performance and outcomes of similar projects.
2. Cost Estimation and Resource Planning:
Data analytics and machine learning can assist in more accurate cost estimation by analyzing historical cost data and project parameters within BIM. This helps in better resource planning and budgeting, minimizing the risk of cost overruns.
3. Performance Analysis and Optimization:
Machine learning algorithms can analyze real-time data from sensors embedded in smart buildings, which is integrated with BIM. This allows for continuous performance monitoring, and insights can be used to optimize energy efficiency, space utilization, and overall building performance.
4. Risk Management:
Data analytics can be employed to assess and predict project risks by analyzing historical data on similar projects. Machine learning algorithms can identify potential risk factors and recommend strategies to mitigate risks, improving overall project management.
5. Supply Chain Optimization:
Machine learning can be applied to optimize the supply chain by analyzing historical data on material usage, delivery times, and costs. This information, when integrated with BIM, allows for better decision-making in sourcing and logistics, improving efficiency and reducing delays.
6. Construction Monitoring and Quality Control:
Machine learning models can analyze construction site data, such as images from drones or surveillance cameras, to monitor progress and quality. Deviations from the planned construction process can be identified early, allowing for timely interventions to maintain quality standards.
7. Facility Management and Maintenance:
Data analytics and machine learning can assist in predicting equipment failures or maintenance needs based on historical performance data from BIM. This proactive approach to facility management helps in minimizing downtime and optimizing maintenance schedules.
8. Occupant Behavior Analysis:
Machine learning algorithms can analyze data on occupant behavior within buildings, such as temperature preferences, space utilization, and traffic patterns. This information, when integrated with BIM, helps in designing and managing spaces that cater to the needs and preferences of occupants.
9. Life Cycle Assessment:
Data analytics and machine learning can contribute to life cycle assessments by analyzing environmental impact data associated with building materials and construction processes. This information aids in designing sustainable buildings and making informed decisions about material choices.
10. Interoperability and Standardization:
Data analytics can be used to streamline interoperability between various BIM software tools by establishing and adhering to data standards. Machine learning can assist in automating data integration processes, making it easier for stakeholders to work with diverse datasets.
The integration of BIM, data analytics, and machine learning holds significant potential to transform the construction and building management industry, providing more efficient, cost-effective, and sustainable solutions across the entire lifecycle of a project.
https://growinresearch.eu/
Dear All, please share the above link among your networks in Ethiopia on fantastic opportunities for PhD fellowships in the Netherlands.
Dear All, please share the above link among your networks in Ethiopia on fantastic opportunities for PhD fellowships in the Netherlands.
While the buildings and construction sector accounts for 30% of global energy use and 27% of emissions, the industry is seizing the moment to make positive change. Green building is a revolution and no construction company can afford to be left behind.....Read more below