Department of Building and Environmental Technology
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- 2020
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Mark
Addressing the risk of maladaptation in Social Protection: The case of World Food Programme
(
- Master (Two yrs)
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Mark
Hur kan infiltration i åkermark beskrivas vid skyfallskartering?
(
- Master (Two yrs)
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Mark
Ersättningsformens inverkan på entreprenörens arbetssätt - Fallstudie på två totalentreprenader
2020) VMTL01 20201(
Division of Building Services
Division of Building Physics
Department of Building and Environmental Technology
Department of Construction Sciences- Bach. Degree
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Mark
Reinforced Concrete Structures Subjected to Imposed Deformations: A Study of Cracking due to Shrinkage in Slab Foundations for Residential Houses
2020) In TVBK-5280 VBKM01 20201(
Division of Structural Engineering
Department of Building and Environmental Technology- Prof. qual. >4 yrs
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Mark
Methods for analysing heavy transports on existing bridges
2020) In LUTVDG/TVBK-20/5279+63 VBKM01 20201(
Department of Building and Environmental Technology
Division of Structural Engineering- Prof. qual. >4 yrs
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Mark
Valet mellan ombyggnad och nybyggnad med hänsyn till hållbarhet
2020) VMTL01 20201(
Division of Building Services
Division of Building Physics
Department of Building and Environmental Technology
Department of Construction Sciences- Bach. Degree
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Mark
Elsäkerhet och Riskhantering - Praktik, regleringar och vetenskapliga teorier för säkrare arbete på elanläggningar
2020) VRSM01 20201(
Risk Management and Safety Engineering (M.Sc.Eng.)
Division of Risk Management and Societal Safety- Master (Two yrs)
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Mark
Praktisk tillämpning av klimatdeklarationen
2020) VMTL01 20201(
Division of Building Services
Division of Building Physics
Department of Building and Environmental Technology
Department of Construction Sciences- Bach. Degree
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Mark
Implementering av digitala stöd med hjälp av BIM 360 & Dalux
2020) VMTL01 20201(
Division of Building Services
Division of Building Physics
Department of Building and Environmental Technology
Department of Construction Sciences- Bach. Degree
-
Mark
Using X-band Radar with a Neural Network to Forecast Combined Sewer Flow - A case study in Lund
(
- Master (Two yrs)